Performance Evaluation of Open source E-commerce application (Konakart) on private cloud

Performance evaluation of open source

E-commerce Framework in Private cloud

infrastructure

By

Onkar Ramesh Kadam

A Project

in

The Department of

Electrical and Computer Engineering

Presented in Partial Fulfillment of the Requirements

for the Degree of Master of Engineering at

Concordia University

Montreal, Quebec, Canada

2013

©Onkar Kadam,2013

Faculty of Engineering and Computer Science

Expectations of Originality

This is to Certify that project prepared by

Onkar Ramesh Kadam

Submitted in partial fulfillment of the requirements for the

degree of

Master of Engineering

Complies with the regulations of this University and meets the

accepted standards with respect to originality and quality

Onkar Kadam, 6614590

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©Onkar kadam, 6614590, Concordia University, [email protected] 1

Acknowledgement

I take this opportunity to express my profound gratitude and deep regards to my guide

Professor Dr Yan Liu for her exemplary guidance, monitoring and constant encouragement

throughout the course of this project. The blessing, help and guidance given by her time to time

shall carry me a long way in the journey of life on which I am about to embark. I thank her for

giving me an opportunity to work on various emerging and booming technologies today.

I also take this to express gratitude towards Department of Electrical and Computer

Engineering, Concordia University for allowing to take up this project towards my Master’s of

Engineering Degree.

Lastly, I thank almighty, my parents, brother, sisters and friends for their constant

encouragement without which this assignment would not be possible.

Onkar Ramesh Kadam

6614590

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Abstract

Performance evaluation of open source E-commerce Framework in Private

cloud infrastructure

Onkar Kadam

The term "Cloud Computing" has been mentioned in relation to services or infrastructural

resources, which can be contracted over a network. Thus, the idea of renting instead of buying IT

is nothing new. The players in the large world of clouds are Software as a Service providers,

outsourcing and hosting providers, network and IT infrastructure providers and, above all, the

companies whose names are closely linked with the Internet's commercial boom. But, all these

services in combination outline the complete package known as Cloud Computing – depending

on the source with the appropriate focus. That which long ago established itself in the private

environment of the Internet is now, noticeably, coming to the attention of businesses too. Not

only developers and startups but also large companies with international activities recognize that

there is more to Cloud Computing than just marketing hype. Cloud Computing offers the

opportunity to access IT resources and services with appreciable convenience and speed. Behind

this primarily, is a solution that provides users with services that can be drawn upon on demand

and invoiced as and when used. Suppliers of cloud services, in turn, benefit as their IT resources

are used more fully and eventually achieve additional economies of scale.

This project is about the evaluation of performance of the open source e-commerce platform

KONAKART over open source private cloud Eucalyptus, using free Application performance

management solution – AppDynamics Lite. This project revolves around how open source

platforms can be used to leverage the performance of ecommerce application at no cost. Also,

this project provides a solution on how an application should be distributed and scaled over the

cloud to handle huge amount of day-to day transactions and traffic. This project can be

foundation for auto-scaling at the Platform as a service level in multi-tenant Cloud

Infrastructures.

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Table Of Contents

List of Figures ................................................................................................................................. 7

Cloud Computing Fundamentals .................................................................................................... 8

1.1. What is “Cloud Computing”? .......................................................................................... 8

1.2. What is a “Cloud”? ........................................................................................................... 9

1.2.1. Types of Cloud deployment models ......................................................................... 9

1.3. Advantages and Features of Cloud Computing Services ................................................. 9

1.4. Cloud Computing Layers ................................................................................................. 9

1.4.1. Infrastructure-as-a-Service ...................................................................................... 10

1.4.2. Software-as-a-Service ............................................................................................. 10

1.4.3. Platform-as-a-Service ............................................................................................. 11

1.5. Characteristics of Cloud Computing .............................................................................. 11

1.5.1. On-Demand self-service ......................................................................................... 11

1.5.2. Broad Network Access ........................................................................................... 11

1.5.3. Resource Pooling .................................................................................................... 11

1.5.4. Elasticity ................................................................................................................. 11

1.5.5. Measured Capacity.................................................................................................. 12

1.6.6. Muti-tenancy ........................................................................................................... 12

1.6. Scalability in cloud systems ........................................................................................... 12

1.7. Virtualization Technology.............................................................................................. 12

1.7.1. Virtual Machines ..................................................................................................... 13

1.7.2. Virtualization platforms .......................................................................................... 13

1.7.3. Virtual Infrastructure Management......................................................................... 13

1.7.4. Cloud Infrastructure Manager ................................................................................. 14

Cloud Computing Infrastructures ................................................................................................. 15

2.1. Types of clouds .............................................................................................................. 15

2.1.1. Public Cloud............................................................................................................ 15

2.1.2. Private Cloud .......................................................................................................... 15

2.1.3. Community Cloud ................................................................................................... 16

2.1.4. Hybrid or Mixed Cloud ........................................................................................... 16

2.2. Case Studies ................................................................................................................... 16

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2.2.1. Amazon Web services in a nutshell (IAAS provider) ............................................ 16

2.2.2. Joyent(IAAS provider ............................................................................................. 17

2.2.3. Rackspace Cloud Servers (IAAS provider). ........................................................... 18

2.2.4. Flexiscale (IAAS provider) ..................................................................................... 18

2.2.5. Gogrid ..................................................................................................................... 18

2.2.6. App Engine ............................................................................................................. 19

2.2.7. Microsoft Windows Azure ...................................................................................... 19

2.2.8. Heroku..................................................................................................................... 20

2.2.9. Aneka ...................................................................................................................... 20

2.2.10. AppScale ............................................................................................................. 21

2.2.11. VMWare Vsphere and Vcloud ............................................................................ 21

2.2.12. Nimbus ................................................................................................................ 21

2.2.13. OpenNebula ......................................................................................................... 22

2.2.14. Eucalyptus ........................................................................................................... 22

Eucalyptus: Open Source, AWS compatible private cloud .......................................................... 24

3.1. Architecture .................................................................................................................... 24

3.1.1. Node Controller(NC) .............................................................................................. 24

3.1.2. Cluster Controller(CC)............................................................................................ 24

3.1.3. Walrus Storage Controller ...................................................................................... 24

3.1.4. Storage Controller(SC) ........................................................................................... 25

3.1.5. Cloud Controller (CLC) .......................................................................................... 25

3.1.6. VMware Broker ...................................................................................................... 25

3.2. Eucalyptus Machine Images (EMI)................................................................................ 25

3.3. Security........................................................................................................................... 26

Monitoring in Cloud Infrastructures ............................................................................................. 28

4.1. Need Of Monitoring in Cloud ........................................................................................ 28

4.2. Abstraction levels of monitoring in Cloud ..................................................................... 30

4.2.1. Low–Level Monitoring ........................................................................................... 30

4.2.2. High-Level Monitoring ........................................................................................... 30

4.3. Commercial Cloud Monitoring Platforms and Services ................................................ 30

4.3.1. Amazon EC2 ........................................................................................................... 30

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4.3.2. Microsoft Azure ...................................................................................................... 31

4.3.3. Rackspace ............................................................................................................... 31

4.3.4. CloudStatus ............................................................................................................. 31

4.3.5. GoGrid .................................................................................................................... 32

4.3.6. Nimsoft ................................................................................................................... 32

4.3.7. AppDynamics Pro ................................................................................................... 32

4.4. Open Source Monitoring Platforms and Services .......................................................... 32

4.4.1. Nagios ..................................................................................................................... 32

4.4.2. OpenNebula ............................................................................................................ 33

4.4.3. CloudStack Zenpack ............................................................................................... 33

4.4.4. Nimbus .................................................................................................................... 33

4.4.5. Dargos ..................................................................................................................... 33

4.5. Monitoring Applications in Cloud infrastructures(PaaS Level) .................................... 34

KonaKart ....................................................................................................................................... 36

5.1. KonaKart Community and Enterprise Versions ........................................................... 36

5.2. KonaKart Enterprise version features ............................................................................ 36

5.3. Architecture .................................................................................................................... 38

5.3.1. Software Architecture ............................................................................................. 38

5.3.2. Deployment Architecture ........................................................................................ 39

5.4. KonaKart Use Case UML Diagrams ............................................................................. 40

5.4.1. Top Level Use Case Diagram ................................................................................. 40

5.4.2. View Items Use Case Diagram ............................................................................... 41

5.4.3. Check-out use case Diagram ................................................................................... 42

AppDynamics-Application Performance management and Monitoring ...................................... 43

6.1. AppDynamics Lite components ..................................................................................... 43

6.1.1. AppDynamics Lite Application Server Agent ........................................................ 43

6.1.2. AppDynamics Lite Viewer ..................................................................................... 43

6.2. AppDynamics Lite Features and Uses ........................................................................... 44

6.3. AppDynamics Lite vs AppDynamics Pro ...................................................................... 46

Performance Evaluation ................................................................................................................ 48

7.1. Deployment Architecture ............................................................................................... 48

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7.2. Test Goals ....................................................................................................................... 49

7.3. Test Environment ........................................................................................................... 49

7.4. Test Phase 1 .................................................................................................................... 49

7.4.1. PaaS Metric results(AppDynamics ......................................................................... 49

7.4.2. IaaS Metric Results ................................................................................................. 51

7.5. Testing Phase 2............................................................................................................... 53

7.6. Testing Phase 3............................................................................................................... 54

7.6.1. Phase 3 Results ....................................................................................................... 54

7.7. Comparing IaaS performance metrics with PaaS performance metrics ......................... 55

Conclusion and Future Work ........................................................................................................ 57

8.1. Conclusion ...................................................................................................................... 57

8.2. Future Work ................................................................................................................... 58

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List of Figures · Fig 1.1 Layers of cloud Computing

· Fig 2.1 Types of Cloud Infrastructures

· Fig 2.2 List of Amazon Web services

· Fig 3.1 logical Eucalyptus architecture

· Fig 5.1 Layers of KONAKART e-commerce application

· Fig 5.2 Sample Multi-tier KONAKART deployment architecture

· Fig 5.3 Top level UML use case diagram for Konakart application

· Fig 5.4 View Items Use case Diagrams

· Fig 5.5 Checkout use case diagram

· Fig 6.1 AppDynamics Lite Components

· FIG 7.1 Deployment Architecture

· Fig 7.2 No of HTTP Requests Vs Web server Throughput

· Fig 7.3 No of HTTP requests Vs Database Server throughput

· Fig 7.4. No of HTTP requests Vs average CPU usage(jiffies)

· Fig 7.5 No of HTTP requests Vs Average System Load

· Fig 7.6 MySql Plugin Metric

List of Tables · Table 6.1 AppDynamics Lite vs AppDynamics Pro

· Table 7.1 ECC Instance Configuration

· Table 7.2 Apache Jmeter load testing results

· Table 7.3 Apache Jmeter load testing results(IAAS metrics)

· Table 7.4 Apache Jmeter Load Testing results Phase 2

· Table 7.5 No of MySql Commands per second

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Cloud Computing Fundamentals 1. Cloud Computing Fundamentals

1.1. What is “Cloud Computing”?

When we use services like Gmail, Facebook, Dropbox, etc., we can say that we are

consuming cloud computing services. The Vendors of such cloud computing services make

provisions such that our data is stored on remote locations and made available to us through

easy-to-use web interface or a web browser. The idea of storing, sharing, using resources

from remote locations or data centers can be termed as Cloud computing. In other words,

Cloud Computing can be defined as on-demand delivery of Information technology (IT)

resources or services over the internet, [1]. The term On-demand is self-explanatory which

means using resources when we need them most and pay for the time we use it. The National

Institute of Standards and Technology, U.S department of commerce defines Cloud

Computing as “Cloud computing is a model for enabling ubiquitous, convenient, on-demand

network access to a shared pool of configurable computing resources (e.g., networks, servers,

storage, applications, and services) that can be rapidly provisioned and released with minimal

management effort or service provider interaction”,[2].In simple words we can define cloud

computing as a service model which enables consumers or users to access a shared pool or

collection of applications(software) and resources(hardware) that can be increased or

decreased dynamically depending on the need of the consumer. In Cloud Computing,

dynamically scalable and also virtualized resources are provided as a service over the internet

[3]. Cloud Computing technology is bound to reshape the IT industry in coming years.

Consumers use a variety of devices, like PCs, laptops, smartphones, and PDAs to access

applications, storage, and application-development platforms over the Internet, via services

offered by cloud computing vendors Technologies such as cluster, grid, and now, cloud

computing, have all aimed at allowing access to large amounts of computing power in a fully

virtualized manner, by aggregating resources and offering a single system view. In addition,

an important aim of these technologies has been delivering computing as a utility. Utility

computing describes a business model for on-demand delivery of computing power;

consumers pay providers based on usage (“pay-as-you-go”), similar to the way in which we

currently obtain services from traditional public utility services such as water, electricity, gas,

and telephony. Cloud computing has been coined as an umbrella term to describe a category

of sophisticated on-demand computing services initially offered by commercial providers,

such as Amazon, Google, and Microsoft. It denotes a model on which a computing

infrastructure is viewed as a “cloud,” from which businesses and individuals access

applications from anywhere in the world on demand [7].The main principle behind this

model is offering computing, storage, and software “as a service.”

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1.2. What is a “Cloud”?

The “Cloud” in cloud computing refers to a set or group of identical devices, in terms of

computing power, assembled or deployed to work together to achieve certain computational

goals.

1.2.1. Types of Cloud deployment models

1.1.1.1. Private Cloud: The cloud infrastructure is deployed for exclusive use by

a single organization comprise of multiple consumers (e.g., business units).It

may be owned, managed, maintained by the organization itself or by a third

party vendor or may be combination of both. [2].

1.1.1.2. Public Cloud: The cloud infrastructure is deployed for open use by the

general public .It may be owned, managed, and operated by a business,

academic, or government organization, or some combination of them. It exists

on the premises of the cloud provider [2].

1.2.1.3. Community Cloud: The cloud infrastructure is deployed for exclusive

use by a specific community of consumers from organizations that have shared

concerns, same research motives. It may be owned, managed, and operated by

one or more of the organizations in the community, a third party, or some

combination of them, and it may exist on or off premises [2].

1.2.1.4. Hybrid Cloud: The cloud infrastructure is a composition of two or more

distinct cloud infrastructures (private, community, or public) that remain

unique entities, but are bound together by standardized or proprietary

technology that enables data and application portability.[2].

1.3. Advantages and Features of Cloud Computing Services:

The expenses to deploy an on-premise computing infrastructure for major organizations is

not an issue as their revenue is very high but is of major hindrance to small business owners

and developers who’d like deploy their services in the market for the people to use, such

small business owners and developers can use cloud computing services i.e third party

services to reach the market. The main feature that differentiates Cloud Computing Services

from traditional Web hosting or storage services is that it is on-demand, i.e, only pay for

how much we use. The main advantage of cloud computing services is that they are fully

managed and maintained by the provider or vendor, the consumer or developer does not

need to worry about the maintenance of the remote servers. Advantages of the cloud

computing technology include cost savings, high availability, and scalability.

1.4. Cloud Computing Layers:

Cloud computing can be viewed as a collection of services, which can be presented as a

layered cloud computing architecture.

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Fig 1.1 Layers of cloud Computing

1.4.1. Infrastructure-as-a-Service:

Cloud Providers or vendors provide access of physical machines, servers, virtual

machines to the cloud user remotely. Hardware is owned, managed and maintained by the

cloud provider. The provider provides access to such physical or virtual machines to the

users for a certain cost which depends upon the computational power of the machines.

Users install all software and utilities related to their work by themselves. And users are

responsible to maintain the software and applications that they deploy on the physical or

virtual machines provided by the vendors. For e.g : Amazon EC2, Netapp, etc

1.4.2. Software-as-a-Service:

The cloud providers or vendors provide access of various software applications and tools

to cloud users via a simple Web browser Interface. Users do not need to download the

application on their local Machine. SaaS allows users to run applications remotely from

the cloud. Problem of accessing the services during high load are resolved by simply

hosting the application and data on multiple servers. For eg: Gmail, google docs,

dropbox.

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1.4.3. Platform-as-a-Service:

Cloud Providers provide a computing platform or software stack to the users such that

they develop and test their own software application on remote servers. PaaS is similar to

IaaS, but also includes operating systems and required services for a particular

application. In other words, PaaS is IaaS with a custom software stack for the given

application. The software stack may include operating system, an integrated development

environment. Vendors can provide a virtual machine or a physical machine as per the

requirements of the user. Users can develop and run their applications on servers

provided by the vendors without having to install environments, servers etc on their local

machine. The main benefits of using a PaaS level cloud services is Zero Infrastructure,

Lower cost and improved profitability, Easy and quick development, Reusability of code

structure and logic, Integration with other web services, Requires no up-front

investments, Centralized information management, Secured and customized access,

Minimize operational costs. [4].

1.5. Characteristics of Cloud Computing:

1.5.1. On-Demand self-service:

On-demand self-service means that customers can request and manage their own

computing resources [2].

1.5.2. Broad Network Access :

Allows services to be offered over the Internet or private networks using wide range of

devices like personal computers, laptops, mobile phones, tablets, workstations, etc [2].

1.5.3. Resource Pooling:

Pooled resources means that consumers draw from a pool of computing resources,

usually in remote data centres. The consumers don’t have knowledge about the exact

location of the resources or the data centres.

1.5.4. Elasticity :

Elasticity in cloud computing is performance property of a service which defines how the

service/application performs when the workload increases or decreases. Basically it

defines the versatility of the cloud to respond to workload changes. Resources are

elastically allotted or released based on the demand of the application or the consumer, in

some cases the allocation and release of the resources is done automatically based on the

current demand of the service,[2]

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1.5.5. Measured Capacity:

The total usage of the resources can be measured, controlled, monitored and reported

continuously so that to provide transparency to the consumers as well as to provide

elasticity based on the current health of the resources.

1.6.6. Muti-tenancy:

In a multi-tenant software architecture, a single software instance is shared by multiple

clients or client organizations, the clients does not have any knowledge of other clients.

The resources of the software stack are virtually divided such that no single thread

interferes with another thread running concurrently.

1.6. Scalability in cloud systems

One of the most essential features of cloud computing services is scalability. Scalability

means that cloud computing offers unlimited processing and storage capacity. Scalability

may refer to increasing the capacity or resources to handle high amount of loads. A scalable

cloud infrastructure is which can easily increase or decrease capacity as per real time

demand of the application or consumer. Providing scalable cloud solutions is not as easy as

its definition. Scalability depends upon many factors like how complex is the architecture,

how organized is the architecture, etc. Scalability deals with the real time performance of a

system (IaaS) or an application (SaaS) or both (PaaS). Providing scalable cloud solution

involves continuously monitoring the real time performance metrics of the physical or

virtual systems or an application. Cloud providers vendors have provisions to monitor real

time performance metrics and based on load perform scaling on the resources, maybe

inward scaling or outward scaling. We will discuss further in depth about cloud computing

in upcoming chapters.

1.7. Virtualization Technology

Virtualization is the idea of partitioning or dividing the resources of a single server into

multiple segregated Virtual machines. Virtualization technology has been proposed and

developed over a relatively long period. The earliest use of VMs was by IBM in 1960,

intended to leverage investments in expensive mainframe computers [8]. The idea was to

enable multitasking – running multiple applications and processes for different users

simultaneously. Robert P. Goldberg described the need for virtual machines in 1974:

“Virtual machine systems were originally developed to correct some of the shortcomings of

the typical third generation architectures and multiprogramming operating systems – e.g.,

OS/360” [9]. Recently, owing to the rapid growth in IT infrastructure, we have seen the

emergence of multicore processors and a wide variety of hardware, operating systems, and

software. In this environment, virtualization has had a resurgence of popularity.

Virtualization can provide dramatic benefits for a computing system, including increased

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utilization, energy saving, rapid deployment, improved maintenance capability, isolation,

and encapsulation. Moreover, virtualization enables applications to migrate from one server

to another while they are still running, without downtime, providing flexible workload

management, and high availability during planned maintenance or unplanned events.

1.7.1. Virtual Machines

A VM is a software implementation of a machine (i.e., a computer) that executes

programs like a physical machine [10]. This differs from a process VM, which is

designed to run a single program, such as the Java Runtime Environment (JRE). A system

VM provides a complete system platform that supports the execution of a complete

operating system (OS). The VM lifecycle has six phases: create, suspend, resume, save,

migrate, and destroy. Multiple VMs can run simultaneously in the same physical node.

Each VM can have a different OS, and a Virtual Machine Monitor (VMM) is used to

control and manage the VMs on a single physical node. A VMM is often referred to as a

hypervisor. Above this level, Virtual Infrastructure Managers (VIMs) are used to

manage, deploy, and monitor VMs on a distributed pool of resources (cluster or data

center). In addition, Cloud Infrastructure Managers (CIMs) are web-based management

solutions on the top of IaaS providers.

1.7.2. Virtualization platforms

Virtualization technology has been developed to best utilize computing capacity. Server

virtualization has been described as follows: “In most cases, server virtualization is

accomplished by the use of a hypervisor (VMM) to logically assign and separate physical

resources. The hypervisor allows a guest operating system, running on the virtual

machine, to function as if it were solely in control of the hardware, unaware that other

guests are sharing it. Each guest operating system is protected from the others and is thus

unaffected by any instability or configuration issues of the others” [12]. Virtualization

methods can be classified into two categories according to whether or not the guest OS

kernel needs to be modified. The two main types of virtualization methods are (1) full

virtualization, (2) Para-virtualization. Full virtualization emulates the entire hardware

environment by utilizing hardware virtualization support, binary code translation, or

binary code rewriting, and thus the guest OS does not need to modify its kernel. Para-

virtualization requires the guest OS kernel to be modified to become aware of the

hypervisor. Because it need not emulate the entire hardware environment, para-

virtualization can attain better performance than full virtualization.[11].

1.7.3. Virtual Infrastructure Management

A Virtual Infrastructure Manager (VIM) is responsible for the efficient management of a

virtual infrastructure as a whole, by providing basic functionality for deploying,

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controlling, and monitoring VMs on a distributed pool of resources. This is done by

communicating with their VMMs.[11]

1.7.4. Cloud Infrastructure Manager

A Cloud Infrastructure Manager (CIM) is a web-based solution focused on deploying

and managing services (deploying, monitoring, and maintaining the VMs) on top of

Infrastructure as a Service (IaaS) clouds. Third-party application-hosting framework

service companies provide higher-level application deployment tools on top of IaaS.[11]

REFERENCES

[1] http://aws.amazon.com/what-is-cloud-computing

[2] The NIST Definition of Cloud Computing, Peter Mell, Timothy Grance.

[3] Handbook of Cloud Computing, Borko Furht, Armando Escalante

[4] http://www.zoho.com/creator/paas.html

[5] http://support.rightscale.com/06-FAQs/FAQ_0043_-_What_is_autoscaling%3F

[6] Auto-scaling Developer Guide , Amazon Web Services.

[7] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, Cloud computing and

emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility,

Future Generation Computer Systems, 25:599_616, 2009

[8] Vmware (2009) http://www.vmware.com/virtualization/history.html.

[9] Goldberg RP (1974) Survey of virtual machine research. IEEE Comput Mag 7(6):34–45

[10] Virtual Machine (Wikipedia) (2009) http://en.wikipedia.org/wiki/Virtual_machine

[11]Nick Antonopoulos , Lee Gillam , Cloud Computing Principles, Systems and Applications,

Springer

[12] IBM white paper (2009) Seeding the Clouds: Key Infrastructure Elements for Cloud

Computing.ftp://ftp.software.ibm.com/common/ssi/sa/wh/n/oiw03022usen/OIW03022USEN.PD

F

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Cloud Computing Infrastructures 2. Cloud Computing Infrastructures

2.1. Types of clouds

Although cloud computing has emerged mainly from the appearance of public computing

utilities other deployment models with variations in physical location and distribution , have

been adopted. In this sense, regardless of its service class , a cloud can be classified as

public , private, community or hybrid based on model deployment shown in the figure

below.

Fig 2.1 Types of Cloud Infrastructures

2.1.1. Public Cloud

Armbrust et al. [2] propose definitions for public cloud as a “cloud made available in a

pay-as-you-go manner to the general public” and private cloud as “internal data center of

a business or other organization, not made available to the general public.”

2.1.2. Private Cloud

Establishing a private cloud means restructuring an existing infrastructure by adding

virtualization and cloud-like interfaces. This allows users to interact with the local data

center while experiencing the same advantages of public clouds, most notably self-

service interface, privileged access to virtual servers, and per-usage metering and billing.

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2.1.3. Community Cloud

A community cloud is “shared by several organizations and supports a specific

community that has shared concerns (e.g., mission, security requirements, policy, and

compliance considerations) [1].”

2.1.4. Hybrid or Mixed Cloud

A hybrid cloud takes shape when a private cloud is supplemented with computing

capacity from public clouds [3]. The approach of temporarily renting capacity to handle

spikes in load is known as “cloud-bursting” [4].

2.2. Case Studies:

2.2.1. Amazon Web services in a nutshell (IAAS provider)

Amazon has a long history of using a decentralized IT infrastructure. This arrangement

enabled our development teams to access compute and storage resources on demand, and

it has increased overall productivity and agility. By 2005, Amazon had spent over a

decade and millions of dollars building and managing the large-scale, reliable, and

efficient IT infrastructure that powered one of the world’s largest online retail platforms.

Amazon launched Amazon Web Services (AWS) so that other organizations could

benefit from Amazon’s experience and investment in running a large-scale distributed,

transactional IT infrastructure. AWS has been operating since 2006, and today serves

hundreds of thousands of customers worldwide. Today Amazon.com runs a global web

platform serving millions of customers and managing billions of dollars’ worth of

commerce every year. Using AWS, you can requisition compute power, storage, and

other services in minutes and have the flexibility to choose the development platform or

programming model that makes the most sense for the problems they’re trying to solve.

You pay only for what you use, with no up-front expenses or long-term commitments,

making AWS a cost-effective way to deliver applications.

It offers a variety cloud services, most notably: S3 (storage), EC2 (virtual

servers), Cloudfront (content delivery), Cloud-front Streaming (video streaming),

SimpleDB (structured datastore), RDS (Relational Database), SQS (reliable messaging),

and Elastic Map-Reduce (data processing). The Elastic Compute Cloud (EC2) offers

Xen-based virtual servers (instances) that can be instantiated from Amazon Machine

Images (AMIs). Instances are available in a variety of sizes, operating systems,

architectures, and price. CPU capacity of instances is measured in Amazon Compute

Units and, although fixed for each instance, vary among instance types from 1 (small

instance) to 20 (high CPU instance). Each instance provides a certain amount of non-

persistent disk space; a persistence disk service (Elastic Block Storage) allows attaching

virtual disks to instances with space up to 1TB. Elasticity can be achieved by combining

the Cloud-Watch, Auto Scaling, and Elastic Load Balancing features, which allow the

number of instances to scale up and down automatically based on a set of customizable

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rules, and traffic to be distributed across available instances. Fixed IP address (Elastic

IPs) are not available by default, but can be obtained at an additional cost. In summary,

Amazon EC2 provides the following features: multiple data centers available in the

United States (East and West) and Europe; CLI, Web services (SOAP and Query), Web-

based console user interfaces; access to instance mainly via SSH (Linux) and Remote

Desktop (Windows); advanced reservation of capacity (aka reserved instances) that

guarantees availability for periods of 1 and 3 years; 99.5% availability SLA; per hour

pricing; Linux and Windows operating systems; automatic scaling; load balancing.[5][6].

Fig 2.2 List of Amazon Web services

2.2.2. Joyent(IAAS provider)

Joyent’s Public Cloud offers servers based on Solaris containers virtualization

technology. These servers, dubbed accelerators, allow deploying various specialized

software-stack based on a customized version of Open-Solaris operating system, which

include by default a Web-based configuration tool and several pre-installed software,

such as Apache, MySQL, PHP, Ruby on Rails, and Java. Software load balancing is

available as an accelerator in addition to hardware load balancers. A notable feature of

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Joyent’s virtual servers is automatic vertical scaling of CPU cores, which means a virtual

server can make use of additional CPUs automatically up to the maximum number of

cores available in the physical host. In summary, the Joyent public cloud offers the

following features: multiple geographic locations in the United States; Web-based user

interface; access to virtual server via SSH and Web-based administration tool; 100%

availability SLA; per month pricing; OS-level virtualization Solaris containers; Open-

Solaris operating systems; automatic scaling (vertical).[6] Joyent uses true cloud-native

technologies, like OS virtualization, to maximize the performance of its computing

resources. Unlike legacy hardware-virtualization designs, the underlying operating

system – SmartOS – runs on bare-metal, meaning there are no extra layers to navigate

before gaining access to hardware resources.[7]

2.2.3. Rackspace Cloud Servers (IAAS provider).

Rackspace Cloud Servers is an IaaS solution that provides fixed size instances in the

cloud. Cloud Servers offers a range of Linux-based pre-made images. A user can request

different-sized images, where the size is measured by requested RAM, not CPU. Cloud

Servers also offers hybrid approach where dedicated and cloud server infrastructures can

be combined to take the best aspects of both styles of hosting as required. Cloud Servers,

as part of its default offering, enables fixed (static) IP addresses, persistent storage, and

load balancing (via A-DNS) at no additional cost.[8],[6]

2.2.4. Flexiscale (IAAS provider)

Flexiscale is a Europe-based provider offering services similar in nature to Amazon Web

Services. However, its virtual servers offer some distinct features, most notably:

persistent storage by default, fixed IP addresses, dedicated VLAN, a wider range of

server sizes, and runtime adjustment of CPU capacity (aka CPU bursting/vertical

scaling). Similar to the clouds, this service is also priced by the hour. In summary, the

Flexiscale cloud provides the following features: available in UK; Web services (SOAP),

Web-based user interfaces; access to virtual server mainly via SSH (Linux) and Remote

Desktop (Windows); 100% availability SLA with automatic recovery of VMs in case of

hardware failure; per hour pricing; Linux and Windows operating systems; automatic

scaling (horizontal/vertical).[9][6].

2.2.5. Gogrid (IAAS provider)

GoGrid, like many other IaaS providers, allows its customers to utilize a range of pre-

made Windows and Linux images, in a range of fixed instance sizes. GoGrid also offers

“value-added” stacks on top for applications such as high-volume Web serving, e-

Commerce, and database stores. It offers some notable features, such as a “hybrid

hosting” facility, which combines traditional dedicated hosts with auto-scaling cloud

server infrastructure. In this approach, users can take advantage of dedicated hosting

(which may be required due to specific performance, security or legal compliance

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reasons) and combine it with on-demand cloud infrastructure as appropriate, taking the

benefits of each style of computing. As part of its core IaaS offerings, GoGrid also

provides free hardware load balancing, auto-scaling capabilities, and persistent storage,

features that typically add an additional cost for most other IaaS providers.[10][6].

2.2.6. App Engine(PAAS provider)

Google App Engine lets you run your Python and Java Web applications on elastic

infrastructure supplied by Google. App Engine allows your applications to scale

dynamically as your traffic and data storage requirements increase or decrease. Google

App Engine supports apps written in several programming languages. With App Engine's

Java runtime environment, you can build your app using standard Java technologies,

including the JVM, Java servlets, and the Java programming language - or any other

language using a JVM-based interpreter or compiler, such as JavaScript or Ruby. App

Engine also features a Python runtime environment, which includes a fast Python

interpreter and the Python standard library. App Engine also features a PHP runtime, with

native support for Google Cloud SQL and Google Cloud Storage that works just like

using a local mySQL instance and doing local file writes. Finally, App Engine provides a

Go runtime environment that runs natively compiled Go code. These runtime

environments are built to ensure that your application runs quickly, securely, and without

interference from other apps on the system.. The App Engine serving architecture is

notable in that it allows real-time auto-scaling without virtualization for many common

types of Web applications. However, such auto-scaling is dependent on the application

developer using a limited subset of the native APIs on each platform, and in some

instances you need to use specific Google APIs such as URLFetch, Datastore, and

memcache in place of certain native API calls. For example, a deployed App Engine

application cannot write to the file system directly (you must use the Google Datastore)

or open a socket or access another host directly (you must use Google URL fetch

service). A Java application cannot create a new Thread either. [11],[6].

2.2.7. Microsoft Windows Azure

Windows Azure delivers a 99.95% monthly SLA and enables you to build and run highly

available applications without focusing on the infrastructure. It provides automatic OS

and service patching, built in network load balancing and resiliency to hardware failure.

It supports a deployment model that enables you to upgrade your application without

downtime .Microsoft Azure Cloud Services offers developers a hosted .NET Stack (C#,

VB.Net, ASP.NET). In addition, a Java & Ruby SDK for .NET Services is also available.

The Azure system consists of a number of elements. The Windows Azure Fabric

Controller provides auto-scaling and reliability, and it manages memory resources and

load balancing. The .NET Service Bus registers and connects applications together.

Windows Azure enables you to use any language, framework, or tool to build

applications. Features and services are exposed using open REST protocols. The

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Windows Azure client libraries are available for multiple programming languages, and

are released under an open source license and hosted on GitHub. Windows Azure

delivers a flexible cloud platform that can satisfy any application need. It enables you to

reliably host and scale out your application code within compute roles. The Windows

Azure Fabric Controller provides auto-scaling and reliability, and it manages memory

resources and load balancing. You can store data using relational SQL databases, NoSQL

table stores, and unstructured blob stores, and optionally use Hadoop and business

intelligence services to data-mine it. You can take advantage of Windows Azure’s robust

messaging capabilities to enable scalable distributed applications, as well as deliver

hybrid solutions that run across a cloud and on-premises enterprise environment.

Windows Azure’s distributed caching and CDN services allow you to reduce latency and

deliver great application performance anywhere in the world.[11][6].

2.2.8. Heroku(PAAS provider)

Heroku is a polyglot cloud application platform. With Heroku, you don’t need to think

about servers at all. You can write apps using modern development practices in the

programming language of your choice, back it with add-on resources such as SQL and

NoSQL databases, Memcached, and many others. Consumers manage your app using the

Heroku command-line tool and you deploy code using the Git revision control system, all

running on the Heroku infrastructure. In the Heroku system, servers are invisibly

managed by the platform and are never exposed to users. Applications are automatically

dispersed across different CPU cores and servers, delivering high performance and

minimizing contention. Heroku has an advanced logic layer than can automatically route

around failures, providing uninterrupted service at all times. [12], [6].

2.2.9. Aneka (PAAS product)

Aneka plays the role of Application Platform as a Service for Cloud Computing. Aneka

supports various programming models involving Task Programming, Thread

Programming and Map-Reduce Programming, which enables a variety of data-mining

and search applications, and tools for rapid creation of applications and their seamless

deployment on private or public Clouds to distribute applications. One of the notable

features of Aneka PaaS is to support provisioning of private cloud resources ranging from

desktops, clusters to virtual datacenters using VMWare, Citrix Zen server and public

cloud resources such as Windows Azure, Amazon EC2, and GoGrid Cloud Service. Each

server in an Aneka deployment (dubbed Aneka cloud node) hosts the Aneka container,

which provides the base infrastructure that consists of services for persistence, security

(authorization, authentication and auditing), and communication (message handling and

dispatching). Cloud nodes can be either physical server, virtual machines (XenServer and

VMware are supported), and instances rented from Amazon EC2. [12][6]

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2.2.10. AppScale (Open Source PAAS)

APPSCALE is an open source distributed software framework or stack used to

implement cloud platform as a service. AppScale is implemented using a collection of

open source technologies like Hadoop, NoSql & Mysql Databases, Apache Zookeeper,

load balancer (haproxy), Mem-cached .etc. AppScale‘s design in based on the Google

App Engine API’s. Using APPSCALE developers can develop their applications

anywhere which are compatible with the Google’s Appengine. Hence, an application

developed on a local AppScale based Virtual Machine can be deployed on any AppScale

based cloud and Google AppEngine, APPSCALE Adds portability to the application and

makes it independent of the cloud beneath. AppScale provides an additional layer of

metrics i.e application level of metrics. AppScale can run on a single a single VM

instance or the can be distributed to multiple instances by assigning different roles to

different VM ‘s and each VM having a Appcontroller to monitor and control the role. The

AppScale platform also provides the scalability, ease of use, and high availability that

users have come to expect from public cloud platforms and infrastructures. This includes

elasticity and fault detection/recovery, authentication and user control, monitoring and

logging, cross-cloud data and application migration, hybrid cloud multitasking, and

offline analytics and disaster recovery. AppScale couple elasticity and fault tolerance to

start/stop platform components within and across VMs, and we ultimately rely on Apache

Zookeeper — which is employed for distributed co-ordination and state management —

for system survivability.

2.2.11. VMWare Vsphere and Vcloud

VMware’s vsphere is suite of tools aimed at transforming IT infrastructures into private

clouds. It distinguishes from other Virtual Infrastructure managers as one of the most

feature-rich, due to the company’s several offerings in all levels the architecture. It

enables IT to meet SLAs (service-level agreements) for the most demanding business

critical applications, at the lowest TCO (total cost of ownership). vSphere accelerates the

shift to cloud computing for existing data centers and also underpins compatible public

cloud offerings, forming the foundation for the industry’s only hybrid cloud model. With

VMware vSphere, a comprehensive set of management tools, and the VMware vCloud

Initiative to ensure cloud compatibility, VMware enables IT organizations to abstract

applications and information from the complexity of the underlying infrastructure –

whether it’s internal or external and deliver applications as a service over which they

have complete control.[13],[14]

2.2.12. Nimbus(IAAS )

In a nutshell, Nimbus allows a client to lease remote resources by deploying virtual

machines (VMs) on those resources and configuring them to represent an environment

desired by the user. It is an open source "infrastructure-as-a-service" (IaaS) solution. Also

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included in a Nimbus installation is a storage cloud implementation called Cumulus that

has been tightly integrated with the other central services .Cumulus is compatible with

the Amazon Web Services S3 REST API, but extends it as well. A set of software needs

to be installed to one service node in a non-root account and a separate piece of software

needs to be installed on any number of virtual machine monitor (VMM) nodes. There is

also a separate download for a client called the cloud client that makes life very easy for

users. Clients interact with the service using credentials over multiple protocols. The

easiest client to use is the cloud client which is geared towards getting users up and

running in minutes. The service must be configured in the cloud configuration to serve

requests from the cloud client. These clients implement web services messaging .But

Nimbus also provides an implementation of Amazon's Elastic Compute Cloud (EC2) that

allows you to use clients developed for the real EC2 system against Nimbus based

clouds. [15]

2.2.13. OpenNebula(Open Source Data Center Virtualization)

OpenNebula is the open-source industry standard for data center virtualization, offering

the most feature-rich, flexible solution for the comprehensive management of virtualized

data centers to enable on-premise Infrastructure as a Service Clouds. OpenNebula

provides many different interfaces that can be used to interact with the functionality

offered to manage physical and virtual resources. OpenNebula provides a powerful,

scalable and secure multi-tenant cloud platform for fast delivery and elasticity of virtual

resources. Multi-tier applications can be deployed and consumed as pre-configured

virtual appliances from catalogs.[16]

2.2.14. Eucalyptus (Open Source private cloud)

“Elastic Utility Computing Architecture Linking Your Programs To Useful Systems” – is

an open-source software infrastructure for implementing cloud computing on clusters.

The current interface to Eucalyptus is compatible with Amazon’s EC2, S3, and EBS

interfaces, but the infrastructure is designed to support multiple client-side interfaces. The

Eucalyptus framework was one of the first open-source projects to focus on building IaaS

clouds. It has been developed with the intent of providing an open-source implementation

nearly identical in functionality to Amazon Web Services APIs. Therefore, users can

interact with a Eucalyptus cloud using the same tools they use to access Amazon EC2. It

also distinguishes itself from other tools because it provides a storage cloud API—

emulating the Amazon S3 API—for storing general user data and VM images. In

summary, Eucalyptus provides the following features: Linux-based controller with

administration Web portal; EC2-compatible (SOAP, Query) and S3- compatible (SOAP,

REST) CLI and Web portal interfaces; Xen, KVM, and VMWare backends; Amazon

EBS-compatible virtual storage devices; interface to the Amazon EC2 public cloud;

virtual networks.[17]

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REFERENCES

[1] P. Mell and T. Grance, The NIST Definition of Cloud Computing, National Institute of

Standards and Technology, Information Technology Laboratory,Technical Report Version 15,

2009

[2] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, and R. Katz, Above the Clouds:A Berkeley

View of Cloud Computing, UC Berkeley Reliable Adaptive Distributed Systems Laboratory

White Paper, 2009.

[3] B. Sotomayor, R. S. Montero, I. M. Llorente, and I. Foster, Virtual infrastructure

management in private and hybrid clouds, IEEE Internet Computing,

13(5):14_22,September/October, 2009

[4] P. T. Jaeger, J. Lin, J. M. Grimes, and S. N. Simmons, Where is the cloud? Geography,

economics, environment, and jurisdiction in cloud computing, FirstMonday, 14(4_5): 2009

[5] http://d36cz9buwru1tt.cloudfront.net/AWS_Overview.pdf

[6] William Voorsluys, James Broberg, and Rajkumar Buyya, Cloud Computing Principles and

Paradigms, 2011, wiley publications

[7] http://www.joyent.com/technology

[8] www.rackspace.com/Cloud_Servers

[9] http://www.flexiscale.com/

[10] http://www.gogrid.com

[11]http://www.windowsazure.com/en-us/overview/what-is-windows-azure/

[12] https://devcenter.heroku.com/articles/quickstart

[13] VMWare Inc., VMware vSphere, the First Cloud Operating, White Paper, 2009

[14] VMWare Inc., VMware vSphere, http://www.vmware.com/products/vsphere/,22/4/2010

[15] http://www.nimbusproject.org/docs/2.10.1/summary.html

[16] http://opennebula.org/documentation:rel4.2:intro

[17] D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L. Youseff, and

D.Zagorodnov, The Eucalyptus open source cloud computing system, in Proceedingsof

IEEE/ACM International Symposium on Cluster Computing and the Grid(CCGrid 2009),

Shanghai, China, pp. 124_131, University of California, Santa Barbara. (2009, Sep.) Eucalyptus

[online]. http://open.eucalyptus.com.

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Eucalyptus: Open Source, AWS compatible

private cloud 3. Eucalyptus: Open Source, AWS compatible private cloud

Eucalyptus is open source software for building, on-premise, AWS-compatible private clouds.

Eucalyptus is a set of web services, modeled after and compatible with Amazon Web Services

(AWS). Eucalyptus is written mostly in Java, Eucalyptus integrates components from over 100

open source projects, tested and packaged into a single easy-to-install and easy-to-use product.

Eucalyptus runs on virtualized infrastructure (Linux +KVM or VMware).Eucalyptus is a

software framework or stack which provides an AWS compatible API which is used to

communicate with various AWS services. Eucalyptus helps in creating and managing a private or

even a publicly accessible cloud. Eucalyptus has become very popular and is seen as one of the

key open source cloud platforms. As Eucalyptus is compatible with AWS, the client tools written

for AWS can be used for Eucalyptus as well.

3.1. Architecture

3.1.1. Node Controller(NC)

Node Controller runs on each node and controls the life cycle of all instances running on

the node. The NC interacts with the OS and the hypervisor (KVM or XEN) running on

the node on one side and the Cluster Controller (CC) on the other side. NC queries the

Operating System running on the node to discover the node's physical resources {the

number of cores, the size of memory, the available disk space and also to learn about the

state of VM instances running on the node and propagates this data up to the CC. The

main function of Node Controller is collection of data related to the resource availability

and utilization on the node and reporting the data to CC. Node Controller is also

responsible for Instance life cycle management.

3.1.2. Cluster Controller(CC)

CC manages single or multiple Node Controllers and deploys/manages instances on

them. CC also manages the networking for the instances running on the Nodes under

certain types of networking modes of Eucalyptus. CC is responsible for Network

management.CC communicates with the Cloud controller (CLC) and the Node controller

CC receives requests from Cloud Controller (CLC) to deploy instances. CC makes

decisions on which NC’s are to be used for deployment of the instances.CC controls the

virtual network available to the instances.CC collects information about the NC’s

registered to it and reports it to CLC.

3.1.3. Walrus Storage Controller(WS3)

WS3 provides a persistent simple storage service using REST and SOAP APIs

compatible with S3 APIs. WS3 is responsible for storing the machine images, snapshots

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and storing and serving files using the S3 API. WS3 can be considered as simple file

storage system. Using Walrus users can store persistent data, which is organized as

buckets and objects. WS3 is a file level storage system, as compared to the block level

storage system of Storage Controller.

3.1.4. Storage Controller(SC)

SC provides persistent block storage for use by the instances. This is similar to the Elastic

Block Storage (EBS) service from AWS. The main function of Storage Controller is to

create persistent EBS devices. It provides the block storage over AoE(ATA-over-

Ethernet) or iSCSI protocol to the instances.SC allows creation of snapshots of volumes.

3.1.5. Cloud Controller (CLC)

The Cloud Controller (CLC) is the front end to the entire Eucalyptus cloud infrastructure.

CLC provides an AWS compliant web services interface to the client tools on one side

and interacts with the rest of the components of the Eucalyptus infrastructure on the other

side. CLC also provides a web interface to users for managing certain aspects of the

cloud infrastructure. The main function of CLC is to monitor the availability of resources

on various components of the cloud infrastructure, including hypervisor nodes that are

used to actually provision the instances and the cluster controllers that manage the

hypervisor nodes .CLC is responsible in deciding which clusters will be used for

provisioning the instances. CLC is also responsible for monitoring the running instances.

CLC has most of the knowledge of the availability and usage of resources in the cloud

and the state of the cloud.[1][2][3][4]

3.1.6. VMware Broker

Eucalyptus framework includes an optional component, the VMware Broker. The

VMware Broker mediates all interaction between Eucalyptus and VMware infrastructure

components (that is, ESX/ESXi, and vCenter). VMware broker is an interface between

Eucalyptus cloud infrastructure and VMware infrastructure.[1][2][3][4]

3.2. Eucalyptus Machine Images (EMI)

A Eucalyptus Machine Image (EMI) is a combination of a virtual disk image(s), kernel and

ram-disk images as well as an xml file containing metadata about the image. These images

are stored on WS3 and are used as templates for creating instances. An EMI is a

combination of following XML’s:

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Fig 3.1 logical Eucalyptus architecture

· An XML file with a name like \linux.img.manifest.xml" with information

about one or more hard disk images, a kernel image and a ram-disk image (id

emi-66441F7E).

· An XML file with a name like “\vmlinuz-2.6.28-11-server.manifest.xml" with

information about the corresponding kernel image (id eki-39FC1255).

· An XML file with a name like \initrd.img-2.6.28-11-server.manifest.xml"

with information about the corresponding ram-disk image (id eri-71ED1644).

Each of these images has its own ID that can be used while running the instances. Since most

enterprise/individual users of Eucalyptus have a need for bringing up instances based on

custom images, image management plays a key role in Eucalyptus administration. Bundling

an EMI is a multi-step procedure involving the following steps:

· Creating a virtual disk image.

· Installing the OS.

· Installing required applications.

· Making the OS ready to run over Eucalyptus cloud .

· Registering the images with Eucalyptus cloud.

· Testing the Image.

3.3. Security

Eucalyptus provides ingress filtering on the instances based on the concept of security

groups which is similar to security groups in AWS. We create custom rules according to our

requirements. You can specify a security group while launching an instance. Each security

group can have multiple rules associated with it. Each rule specifies the source IP/network,

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protocol type, destination ports etc. Any packet matching these parameters specified in a

rule is allowed to access the eucalyptus instance. Rest of the packets are blocked. A security

group that does not have any rules associated with it causes blocking of all incoming traffic.

The security group mechanism does not provide any egress filtering, so all outgoing traffic

from the instance is allowed.[1][2][3][4]

REFERENCES

[1] www.eucalyptus.com

[2]http://www.eucalyptus.com/docs/eucalyptus/3.3/admin-guide/

[3] http://www.eucalyptus.com/docs/eucalyptus/3.3/user-guide/

[4] https://github.com/eucalyptus

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Monitoring in Cloud Infrastructures 4. Monitoring in Cloud Infrastructures.

Monitoring of Cloud is a task of great importance for both Cloud service providers and Cloud

users. It is a key aspect for controlling, maintaining and managing hardware and software

infrastructures. Monitoring provides information and key performance parameters for platforms

and applications. Real-Time monitoring of the Cloud and of its SLAs provides both the Vendor

and the Consumers with information such as the workload generated by the latter and the

performance and QoS offered through the Cloud, also allowing to implement mechanisms to

prevent or recover violations and breakdowns. Cloud Computing involves many activities for

which monitoring is an essential task.

4.1. Need Of Monitoring in Cloud

4.1.1. Capacity and Resource Planning

One of the most challenging tasks for application and system developers, before the large

scale adoption of Cloud Computing, has always been resource and capacity planning. In

order to guarantee the performance required by applications and services, developers

have to (i) quantify capacity and resources (e.g. CPU, memory, storage, etc.) to be

purchased, depending on how such applications and services are designed and

implemented, and (ii) determine the estimated workload for the application or service.[1].

However, while an estimation can be obtained through static analysis, testing and

monitoring, the real values are unpredictable and highly variable. Cloud vendors usually

offer guarantees in terms of QoS and thus of resources and capacity for their services as

specified in SLAs[2], and they are in charge of their resource and capacity planning so

that service and application developers do not have to worry about them[3]. Monitoring is

the most important aspect for Cloud vendors to predict and keep track of the evolution of

all the parameters involved in the process of QoS assurance.

4.1.2. Capacity and Resource Management

The initial step to manage a complex system like a Cloud consists in having a monitoring

system able to accurately capture its real time state,[4]. Virtualization has become a key

component to implement Cloud Computing. Hiding the high heterogeneity of resources

of the physical infrastructure, virtualization technologies introduced another complexity

level for the infrastructure provider, which has to manage both physical and virtualized

resources [1]. Virtualized resources may migrate from a physical machine to another at

any time. Hence, in Cloud Computing scenarios monitoring is necessary to cope with

volatility of resources and fast changing network conditions. The main concern of Cloud

vendors is to provide 100% uptime to the consumer’s application.

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4.1.3. Data Center Management

Cloud services are provided through large scale data centers, located at multiple remote

places, whose management is a very important activity. Actually, this activity is part of

resource management and we reported it here because of its importance and of its

peculiar requirements. Data center management activities (e.g. data center control) imply

two fundamental tasks: (i) monitoring, that keeps track of desired hardware and software

metrics; (ii) data analysis, that processes such metrics to infer system or application states

for resource provisioning, troubleshooting, or other management actions [5]. In order to

properly manage such data centers, both monitoring and data analysis tasks must support

real time operation and scale up to tens of thousands of heterogeneous nodes, dealing

with complex network topologies and I/O structures.[1]

4.1.4. SLA Management

Monitoring may allow Cloud providers to formulate more realistic and dynamic SLAs

and better pricing models by exploiting the knowledge of user perceived performance [6]

4.1.5. Billing.

One of the most essential characteristics of Cloud Computing is to offer “measured

services”, allowing the Consumer to pay proportionally to the use of the service with

different metrics and different granularity, according to the type of service and the price

model adopted [1].

4.1.6. Troubleshooting

A comprehensive, reliable and timely monitoring platform is therefore needed for

Providers to understand where to locate the problem inside their complex infrastructure

and for Consumers to understand if any occurring performance issue or failure is caused

by the Provider, network infrastructure, or by the application itself.[7].

4.1.7. Performance and Security Management

Monitoring the perceived performance of the cloud is necessary to adapt to the changes

or to apply corrective measures to overcome failures. Monitoring is then necessary since

it may considerably improve the performance of real applications [8] and affect activity

planning and repeatability of experiments. Cloud security is very important for a number

of reasons. Security is considered as one of the most significant obstacles to the spread of

Cloud Computing, especially considering certain kinds of applications. For hosting

critical services for public agencies, Clouds have to satisfy strict regulations and prove it.

And this can be done through a monitoring system that enables auditing.

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4.2. Abstraction levels of monitoring in Cloud

Monitoring can be done on two different levels, depending on the beneficiary of the

monitoring information.

4.2.1. Low–Level Monitoring

Low level monitoring is related to information collected by the Cloud Vendor and

usually not exposed to the Consumer, and it is more concerned with the status of the

physical infrastructure of the whole Cloud (e.g. servers and storage areas, etc.). In the

context of IaaS, both levels are of interest for both Consumers and Providers. Low level

monitoring specific utilities collect information at the hardware layer, at the operating

system layer and at middleware layer, at the network layer and at the facility layer. For

commercial Cloud providers, the low-level monitoring service is usually kept

confidential.

4.2.2. High-Level Monitoring

High-level monitoring information is typically interesting for Cloud consumers. High

level monitoring is related to information on the status and health of the virtual platform.

This information is collected at the middleware, application and user layers by Providers

or Consumers through platforms and services operated by themselves or by third parties.

In the case of SaaS, high level monitoring information is generally of more interest for

the Consumer than for the Provider.

4.3. Commercial Cloud Monitoring Platforms and Services

Commercial Platforms implements both low-level and high level monitoring.

4.3.1. Amazon EC2

As a commercial cloud, Amazon does not provide any information about low-level

monitoring system that it uses to gather information of the physical infrastructure. The

way the monitoring data is collected and analyzed is kept confidential. The approach that

Amazon has adapted with respect to high-level resource monitoring is to provide a

service called CloudWatch , in which collected information are mainly related to the

virtual platforms,[9]. CloudWatch is able to monitor Amazon services like EC2, Elastic

Load Balancing and Amazon's Relational Database Service. CloudWatch gathers several

kinds of monitoring information and it stores them for two weeks. On these data, users

can build plots, statistics, indicators, temporal behaviors, thresholds, alarms, etc.. Alarms

can trigger specific actions like event notification, through the Amazon SNS service, or

Auto-scaling [9]. CloudWatch is actually a generic mechanism for measurement,

aggregation and querying of historic data. All the measurements are aggregated together

over a period of one minute.[9]. In association with the Elastic Load Balancer service and

the Auto-scaling feature, CloudWatch can be configured to automatically replace

unhealthy platform instances with new instances. CloudWatch comes with an alarm

feature. An alarm has a number of actions that are triggered when a measure of a

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parameter acquired by the monitoring service increases over a threshold or decreases

under a threshold. The measures of the parameter that the CloudWatch is supposed to

monitor, are configurable and the thresholds correspond to configurable limits for these

measures. The possible actions are either a scaling action (Auto-scaling) or a notification

action(messaging alert).The basic monitoring plan (Free Tier) is free of charge in which

cloudwatch monitors basic metrics over a sampling rate of five minutes. An advanced

plan is charged in which cloudwatch provides advanced performance metrics and one

minute sampling rate,[10].

4.3.2. Microsoft Azure

The information about the low level monitoring system is not disclosed by Microsoft. For

monitoring applications deployed on Microsoft Windows Azure cloud infrastructure, the

application developer is given a software library that facilitates application diagnostics

and monitoring for Azure applications. This library is integrated into the Windows Azure

SDK. It features performance counters, logging, and log monitoring. Some third party

monitoring services are developed around the software library. Among many,

AzureWatch is a monitoring service that is mostly used by the developers,[11].

AzureWatch monitors and aggregates key performance metrics from Azure resources like

instances, databases, database federations, storage, websites and web applications.

Applications. It supports user defined performance counters related to application

metrics. According to the information available on the website, it explicitly addresses

Scalability, Adaptability, Autonomicity , and Extensibility.[11]

4.3.3. Rackspace

The information about the low-level monitoring entity has not made public. RackSpace,

uses CloudSites to provide its consumers with monitoring data like CPU utilization and

traffic volume. In addition, RackSpace provides tools, called Cloud tools, able to build a

complete monitoring solution with specific focus on virtual machines and alerting

mechanisms. RackSpace has recently acquired CloudKick [12], a multi-- Cloud

management platform with a wide range of both high and low level monitoring features

and metrics, and provisions to develop custom plugins. Monitoring data can be visualized

in real time and alert systems can be configured to inform users in real time (e.g. through

email or SMS) [12]. The platform mainly provides Scalability and Adaptability.

4.3.4. CloudStatus

It is the first independent Cloud monitoring service which can be used to monitor

Amazon web Services and Google’s App Engine. It is built on top of Hyperic HQ. It

provides monitoring of user application performance, a methodology for evaluating the

root cause analysis of performance changes and degradations, and both real time and

weekly trends of monitored metrics. The main feature of such platform is Timeliness.[13]

FALL 2013


4.3.5. GoGrid

GoGrid has not disclosed the low-level monitoring system that it uses to monitor the

physical infrastructure. GoGrid runs a collaboration program. The name of this program

is GoGrid exchange[14].This program provides third party services that can be useful for

cloud consumers. Some of the services offered by the program include monitoring

features ranging from platform security monitoring, resource usage monitoring and

database monitoring. These services also include the possibility of configurable alerts

based on the values of the monitored metrics.

4.3.6. Nimsoft.

Nimsoft Monitor dramatically improves service quality and reduces the costs of IT

service delivery by utilizing a single, unified IT monitoring solution spanning both

traditional data centers and newer virtualization and cloud environments. CA Nimsoft

Monitor allows you to proactively monitor the quality of service delivered by your entire

IT environment including servers, applications, networks, databases, storage, cloud

environments and end user experience. IT professionals and business users gain end-to-

end visibility into their IT services using our customizable portals, dashboards and

reports. Nimsoft provides features like scalability and multi-tenancy.[15]

4.3.7. AppDynamics Pro

AppDynamics Pro is an advanced Application performance monitoring service.

AppDynamics can be used to monitor single system, or an entire datacenter or a Cloud

infrastructure. AppDynamics is independent of the underlying cloud fabric.

AppDynamics Pro provides real time monitoring of many types of applications

specifically database oriented applications. Detail information about AppDynamics is

provided in coming chapters.

There are many other commercial monitoring services like Monitis, LogicMonitor, Aneka,

GroundWork, etc. that can be used as a monitoring solution for cloud infrastructures.

4.4. Open Source Monitoring Platforms and Services

4.4.1. Nagios

Nagios is a powerful monitoring system that enables organizations to identify and resolve

IT infrastructure problems before they affect critical business processes[16].

Nagios has been extended with monitoring capabilities for both virtual instances and

storage services for cloud infrastructures.[17]. Thanks to such extensions it has been

adopted for monitoring Eucalyptus, a well known open source platform for Cloud

Computing, compatible with both EC2 and S3 Amazon services. It is also used for

monitoring OpenStack , an open source Cloud platform for IaaS.

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4.4.2. OpenNebula

OpenNebula is an open-source project used for building and managing virtualized

enterprise data centers, enterprise private, public and hybrid clouds.[18]. Using as entity

called Information Manager, it monitors Cloud physical infrastructures and provides

information to Cloud providers. Monitoring data are collected through probes installed on

the nodes, queried through SSH connections, and they are related to information

concerning the status of physical nodes. It provides Scalability and Adaptability as main

features.[1].

4.4.3. CloudStack Zenpack

CloudStack [19] is an open source software written in Java, designed to deploy and

manage large networks of virtual machines, as a highly available and scalable Cloud

platform. It currently supports the most popular hypervisors (e.g. VMware, Oracle VM,

KVM, XenServer, and Xen Cloud Platform), and offers three ways to manage Cloud

computing environments: via a web interface, a command line tool, and a RESTful API.

In order to monitor CloudStack virtual and physical devices, a Zenoss extension called

ZenPack [20] can be used. It manages both alerts and events and provides the parameters

(aggregated from all zones, pods, clusters and hosts) related to the memory, CPU, and

storage, as well as to the network. The main feature offered by the CloudStack ZenPack

is Timeliness.

4.4.4. Nimbus

The Nimbus [21] platform is an integrated set of tools (application instantiation,

configuration, monitoring, repair, etc.) to implement infrastructure Clouds for scientific

users supporting the combination of OpenStack, Amazon, and other Clouds.

4.4.5. Dargos

DARGOS [22] is a distributed Cloud monitoring architecture using a hybrid push/pull

approach to disseminate resource monitoring information. DARGOS provides measures

of the physical and virtual resources in the Cloud while maintaining a low overhead. In

addition, it has been designed to be flexible and extensible with new metrics easily.

DARGOS ensures an accurate measurement of physical and virtual resources in the

Cloud keeping at the same time a low overhead. In addition, DARGOS is flexible and

adaptable and allows defining and monitoring new metrics easily. The proposed

monitoring architecture and related tools have been integrated into a real Cloud

deployment based on the OpenStack platform [22].

Some of other open source monitoring services are Ganglia, Hyperic HQ, PCMONS,

Sensu,etc.

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4.5. Monitoring Applications in Cloud infrastructures(PaaS Level):

Businesses are driving their IT operatives to improve performance and boost productivity by

becoming increasingly application-centric, a radical change from the infrastructure-centric

approach they had earlier. At the same time, however, applications themselves are becoming

increasingly difficult to manage as they move toward highly-distributed, multi-tier, multi-

element constructs that in many cases rely on application development frameworks such as

Microsoft .NET or Java. Moreover, businesses try to host multiple applications on a single

physical machine or virtual machine, to minimize the cost. Small businesses outsource the

hosting of their application to public datacenter or public clouds to further lower the costs.

Virtualization has furthermore reduced the cost for hosting applications in cloud. Multi-

tenancy has become one of the main features of public data centers and cloud infrastructure.

In a multi-tenancy architecture, multiple cloud clients share computing resources. The main

features a multi-tenant cloud architecture should satisfy are scalability, security and cost

effectiveness. In reality, because of the cost-effectiveness, businesses are migrating to public

cloud infrastructures. But affordability comes at a price –“sharing of resources”. To provide

scalability, in a multi-tenant architecture, real time monitoring at the PaaS level is required

such that applications can be scaled horizontally or vertically. Application performance

management (APM) is a discipline within systems management. It focuses on the

monitoring and availability of software applications. APM looks at how fast transactions are

completed for an end user or how fast information is delivered to the end user, via a

particular network or web services infrastructure. Virtualization and sharing of resources has

added more to already existing complexity of cloud infrastructures. Application

performance management or monitoring tools have become of great importance.

REFERENCES

[1] Giuseppo Aceto,Alessio Botta, Walter de Donato, Antonio Pescapè1 University of Napoli

Federico II, Cloud Monitoring: a Survey

[2] P.Hasselmeyer and N. d'Heureuse, "Towards holistic multi-- tenant monitoring for virtual

data centers," in Network Operations and Management Symposium Workshops (NOMS Wksps),

2010 IEEE/IFIP, Apr. 2010,pp. 350 356

[3] J.Shao and Q.Wang, "A performance guarantee approach for cloud applications based on

monitoring," in Computer Software and Applications Conference Workshops (COMPSACW),

2011 IEEE 35th

Annual, Jul. 2011, pp. 25 30.

[4] A.Viratanapanu, A.K. A.Hamid, Y. Kawahara, T. Asami, On demand fine grain resource

monitoring system for server consolidation. Kaleidoscope: Beyond the Internet? Innovations for

Future Networks and Services, 2010 ITU T,IEEE, pp.1 8

FALL 2013


[5] C.Wang,K.Schwan,V. Talwar, G. Eisenhauer, L.Hu,and M.Wolf: A flexible architecture

integrating monitoring and analytics for managing large scale data centers, Proceedings of

ICAC, 2011.

[6] A.Khurshid, A.Al Nayeem,and I. Gupta, "Performance evaluation of the Illinois cloud

computing testbed," unpublished, Tech. Rep., Jun.2009

[7] L.Romano.D. D.Mari, Z. Jerzak, and C. Fetzer,“A novel approach to QoS monitoring in the

cloud”, Data Compression, Communications and Processing, International Conference on, vol. 0,

pp. 45 51, 2011.

[8] J.Schad, J.Dittrich,and J. A. Q. Ruiz, "Runtime measurements in the cloud: observing,

analyzing, and reducing variance," Proc. VLDB Endow., vol. 3, no. 1 2, pp. 460 471,Sep.2010.

[9] http://awsdocs.s3.amazonaws.com/AmazonCloudWatch/latest/acw---dg.pdf

[10] http://aws.amazon.com/pricing/cloudwatch/

[11] http://www.paraleap.com/azurewatch.

[12] https://www.cloudkick.com/home

[13]http://www.hyperic.com/products/cloud-status-monitoring

[14] http://exchange.gogrid.com/

[15] http://www.ca.com/us/lpg/nimsoft.aspx

[16] http://www.nagios.org/about/

[17] Frédéric Desprez, Eddy Caron, Luis Rodero-Merino Adrian Muresan, “Auto scaling, load

balancing and monitoring in commercial and open source clouds”, CRC Press, Chapter in

"Cloud computing: methodology, system and applications" book, 2011.

[18] http://opennebula.org/about:about

[19] http://www.cloudstack.org/

[20] https://github.com/zenoss/ZenPacks.zenoss.CloudStack

[21] http://www.nimbusproject.org/

[22] http://community.rti.com/paper/dargos-highly-adaptable-and-scalable-monitoring-

architecture-multi-tenant-clouds

FALL 2013


KonaKart 5. KonaKart

KonaKart is a software application that implements an enterprise java eCommerce / online

shopping cart system. It's main modules are:[1][2]

· A shop web application used by customers to buy your products online.

· An Administration application to enable you to manage your online store.

· Many Customization and Extension features.

5.1. KonaKart Community and Enterprise Versions

KonaKart comes in two separate installations/versions viz , A free Community Edition

which can be downloaded from the KonaKart website and an Enterprise edition which can

be purchased from the KonaKart website. The Community Edition is intended for use by

small businesses and charitable organizations. A condition of the license agreement is to

display "Powered By KonaKart" with a link to the KonaKart web site, on the main page of

the online store. The Enterprise Extensions are available as a separate installation kit which

is installed on top of the community edition to provide more features and functionality. The

"Powered by KonaKart" link is not mandatory for a KonaKart based store when the

Enterprise Extensions are installed over the Community version. The full source code of the

storefront application including the KonaKart client engine, the Struts action classes, the

JSPs, the payment modules, order total modules and shipping modules are included in the

Enterprise Edition of KonaKart. The Community Edition includes all of the above except

the source of the client engine. KonaKart supports most popular databases through JDBC.

(e.g. MySQL, PostgreSQL, Oracle, DB2, MS SQL Server are all supported in the download

package). Written in Java. needs a servlet engine such as Apache Tomcat to run. Modular

approach with APIs at various levels. The APIs are available as Java APIs, SOAP, JSON

and RMI. The JSON APIs are used by the jQuery plugin that allows the KonaKart

application engine to be called using AJAX JavaScript calls. The variety of protocols

supported, promote connectivity even from outside of the company firewall and allow client

side applications (i.e. .Net, MS Excel etc.) to use the KonaKart engine.[1][2]

5.2. KonaKart Enterprise version features:[1][2]

· KonaKart Client Engine source code which includes the full source code of the

client engine as well as a utility for creating an Eclipse project for customizing the

storefront application.

· Multi-Store mode allows you to run an unlimited number of stores with a single

KonaKart installation and a single database schema.

· Indexed search using Lucene search technology gives web users a lightning fast

search experience even for very large product catalogs. As you type into the

FALL 2013


search box, a list of suggested search items appear matching the typed letters. The

suggestions are weighted by popularity so the most common suggestions are

shown first.

· Advanced marketing functionality that allows you to capture customer data as the

customer uses your KonaKart eCommerce store; and to use that data within

complex expressions in order to show dynamic content, activate promotions and

send eMail communications.(Real Time Data Processing).

· Promotion evaluation directly for products, rather than as Order Total modules.

This allows a customer to view the available promotions for a product without

having to add it to the cart.

· Unlimited number of custom product attributes. Each attribute may include

metadata for validation and widget selection during data entry using the Admin

App.

· Unlimited number of miscellaneous objects may be associated with products and

categories.

· Wish List functionality.

· Gift Registry functionality.

· Reward Points (redeemable by the customers during checkout)

· Gift Certificates.

· The KonaKart APIs are available via Java RMI (Remote Method Invocation),

JSON (application engine only with JSON) and JavaScript.

· jQuery plugin.

· Shopping Widgets.

· Job Scheduling.

· Support for Recurring Billing.

· Google Base integration which allows you to publish your product information

for inclusion in Google search results (Search Engine Optimization)

· Product Synchronization feature to allow the synchronization of products between

pre-production and production environments.

· XML Import/Export feature for KonaKart objects such as product, customer,

order etc.

· Java Message Queue Integration (Apache ActiveMQ) to support the guaranteed

delivery of messages to external systems.

· The language of the admin app may be changed dynamically.

· PDF invoices can be created and sent to customers as email attachments and d•

LDAP module to connect to an LDAP directory in order to validate customer and

admin user credentials downloaded from the storefront application.

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5.3. Architecture:

5.3.1. Software Architecture

KonaKart has a modular architecture consisting of different software layers as can be

seen in the diagram below. Source code is provided for the blocks colored in light blue.

The diagram shows how storefront applications written in Java and other technologies

may interface to the KonaKart engines using one of the supported API technologies.

Fig 5.1 Layers of KONAKART e-commerce application

The KonaKart Server is a multi-threaded component that contains the core functionality

of the e-commerce application. It exposes a SOAP Web Service interface, an RMI

interface, a JSON interface and a Java API. It interfaces to a persistence layer and plug

in modules for calculating shipping costs, promotional discounts and for connecting to

payment gateways. The persistence layer supports databases from many different

database vendors such as Oracle, Microsoft’s SQL Server, DB2 from IBM, MySQL,

FALL 2013


PostgreSQL and many others. The KonaKart Client manages the state of a user

(associated with the user’s session) as he navigates around the application. Struts

[http://struts.apache.org/] is a popular framework that implements the Model-View-

Controller (MVC) architecture. The source code of the Struts Action classes (for the

store front application [http://www.konakart.com/konakart/Welcome.action]) is

included in the download package in order to provide examples of how to call the

KonaKart Client API. The store front application uses JSPs to generate the UI.

However, different technologies can easily be implemented thanks to the modular

approach of KonaKart.[1][2]

5.3.2. Deployment Architecture

KonaKart can be deployed on a single machine but usually in a production environment

KonaKart is deployed in a distributed manner.

Fig 5.2 Sample Multi-tier KONAKART deployment architecture.

FALL 2013


5.4. KonaKart Use Case UML Diagrams

5.4.1. Top Level Use Case Diagram

Fig 5.3 Top level UML use case diagram for Konakart application.

1.

Login

Add to

Cart

Checkout

Register

View

Items

Customer

Authentication

Identity

Provider

Payment Service

kout

Exten

d

include

Extend

Includ

Extend

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5.4.2. View Items Use Case Diagram

Fig 5.4 View Items Use case Diagrams

Search Items

Browse sections

Browse New

Arrivals

Browse

Featured

Product

Browse Items

on sale

Add to cart View Items

Extend

Extend

Extend

Extend

Extend

Extend

Extend

Extend

Extend

Extend

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5.4.3. Check-out use case Diagram

Fig 5.5 Checkout use case diagram

REFERENCES

[1]www.konakart.com

[2]Konakart user and Developer guide

Login

Register

View/Upd

ate cart

Shipping

calculation

Pay By

Debit

Payment

Pay By

Credit

Checkout

Ch kokoutkout

Include

Include

Include

Include

Include

Include Include

Include Include

Include

FALL 2013


AppDynamics-Application Performance

management and Monitoring

6. AppDynamics - Application Performance Management and Monitoring

AppDynamics is unique application performance management solution. The combination of

intelligent technology and an intuitive user interface make AppDynamics the best solution for

managing modern applications.[1] AppDynamics is the next-generation application performance

management solution, used by leading business organizations to manage and monitor the

performance and availability of their revenue-critical Java applications.[2].AppDynamics is

available as two editions viz ., AppDynamics Lite (free) and AppDynamics Pro (commercial).

AppDynamics is one of the first APM built for highly distributed service-oriented environments.

It enables fast root cause diagnostics at the method/class level, while creating no more than 2%

overhead, even in high-volume production deployments.[2]. AppDynamics can dynamically

scale them in cloud and virtual environments.

6.1. AppDynamics Lite components

AppDynamics Lite enables its users to monitor application requests in real time. It organizes

these requests into Business Transactions and calculates the health of each transaction based

on the number and rate of slow or stalled requests or those with errors. AppDynamics Lite

provides continuous automatic diagnostic visibility and helps you drill down to find the root

cause of the problem [2]. The AppDynamics Application Server Agent collects performance

data overall and diagnostic data and sends it to the AppDynamics Lite Viewer where you

can monitor the health of each Business Transaction from a single dashboard. You can reach

the root of all Business Transactions to troubleshoot code hot spots, exception stack traces,

and SQL queries.[2]

6.1.1. AppDynamics Lite Application Server Agent

The main function of Application Server Agent is to collect performance statistics, data

about business transactions, and application performance data. Users install and run the

agent inside of a JVM or CLR process on an application server. The Agent and Viewer

communicate through a one-way standard HTTP connection (Agent to Viewer). The

Agent sends all the performance data that it monitors to the Viewer for storage and

analysis [2].

6.1.2. AppDynamics Lite Viewer

The Viewer stores and analyzes application performance data about a single JVM or

CLR, using data collected by the Agent. You install the Viewer on the same machine as

the application server or Java process that you want to monitor. The Viewer requires very

little disk space and memory, and you can open multiple Viewers on multiple application

FALL 2013


servers running on the same machine. The Viewer is a browser-based Flash application

that runs on commonly-used computer systems. To monitor multiple applications running

on the same machine user can start Liteviewer of separate ports. Every application has

their own liteviewer and appagent.

Fig 6.1 AppDynamics Lite Components

6.2. AppDynamics Lite Features and Uses:

6.2.1. Organizing User Requests Into Business Transactions

To simplify and organize the flow of traffic, AppDynamics Lite groups all application

requests into categories of requests called business transactions. AppDynamics Lite

applies automatic discovery rules for identifying the entities in a business application.[2]

6.2.2. Monitoring Business Transaction Health

AppDynamics Lite measures business transaction health based on the individual requests

for that transaction. If an ‘Add to cart’ operation from a shopping cart is identified as a

business transaction, AppDynamics Lite reports how many AddTocarts were normal,

slow, very slow or had errors. It also monitors stalled checkouts.[2]. AppDynamics Lite

tracks these metrics for different time ranges across a 2-hour period and displays a

graphical health indicator on the dashboard against the business transaction.

FALL 2013


6.2.3. Monitoring Backend Activity

Outbound calls from an App Server can be very performance intensive. In addition, they

are sensitive to availability of resources such as connections and sockets. Any delays or

errors in the external backends result in immediate performance problems for the

application. AppDynamics Lite monitors external calls from the monitored App Server to

standard backends.[2]

6.2.4. Identifying And Diagnosing Slow Requests

Identifying slow requests is an important part of monitoring business transaction health.

Slow requests are caused by problems with code or its design, database queries, resource

contention, etc. AppDynamics Lite watches every request to isolate which requests were

slow or very slow and can keep track of the total slow requests for a two-hour period.

AppDynamics Lite monitors two levels of slowness(slow or very slow) to separate a bad

user experience from a worse user experience. To detect slow requests, AppDynamics

Lite compares the time taken to complete a request and marks it slow or very slow

depending on a set of thresholds. It sends diagnostic information to the Viewer.

AppDynamics Lite supports both dynamic and static thresholds to determine slow

requests. It supports a global value only where there is a common threshold for slow or

very slow requests across all business transactions.In a scenario where there are a lot of

frequent slow requests, AppDynamics Lite captures a limited number of the slow

requests to ensure low system overhead.[2]

6.2.5. Identifying And Diagnosing Stalls

Stalls are highly visible application performance problems since there is often no

feedback to the end user for an unacceptable amount of time. The most common reasons

for stalls in are not able to get a connection from a connection pool because of limited

resources and not being able to connect to an external backend because of network issues.

There are many other issues that can cause stalls. AppDynamics Lite isolates which

application requests have stalled and keeps track of the total number of stalls for a two-

hour period.[2].

6.2.6. Isolating Root cause

AppDynamics Lite captures diagnostic data about bad user requests and presents it in call

graphs. You can analyze these detailed call graphs to determine the root cause of

problems. AppDynamics Lite provides snapshots, which are call graphs with high

amount of detail.[2].

6.2.7. Setting alerts and email notification

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AppDynamics Lite enables the user to set up custom alerts for custom metrics.An Email

notification system can be set if thresholds of the metrics are met.

6.2.8. Identifying and diagnosing errors

Identifying and being able to look at the root causes of errors in real time helps system

developers to resolve them faster, without having to scan through log files. AppDynamics

Lite watches for errors including exceptions, error log entries, and HTTP return codes. In

scenarios where there are many errors, AppDynamics Lite captures a limited number of

them to maintain an acceptable overhead.[2]

6.3. AppDynamics Lite vs AppDynamics Pro

Here are 14 key differences between the two editions.[3]

AppDynamics Lite AppDynamics Pro

Lite is typically deployed on the users

desktop machine

Pro requires a dedicated management server

that can either be managed as a service by

AppDynamics or by the user on-premise

Lite provides visibility for a single JVM or

IIS web server;

Pro provides complete visibility of every

JVM, IIS web server, CLR, and tier across

your distributed application.

Lite is limited to 30 business transaction

types.

Pro is unlimited.

Lite is limited to Server visibility Pro can monitor the User Experience across

the Browser, Network and Server.

Code Execution and Call stack visibility are

limited to a single JVM and IIS web server

in Lite

With Pro, users can follow code execution

across every JVM, IIS instance and CLR

while maintaining full business transaction

context.

Lite and Pro for Java now both offer real-

time monitoring of JMX metrics.

Pro in addition, allows users to perform

historical and long term trending with

baselines overtime, and also with other

metrics such as host OS, application metrics

and CLR counters for .NET.

Thresholds in Lite are driven by a single

static threshold for all business transactions.

In Pro, every business transaction has its

own dynamic baseline. This feature ensures

that AppDynamics can learn your

application’s normal behavior and only send

alerts when real problems occur.

Lite now provides basic alerting capabilities

for simple thresholds on the application,

business transactions and

JMX metrics (for Java)

Pro provides more advanced features such as

rule based policies for more granular alerting

along with launch in context links for rapid

alert triage.

Diagnostic sessions and data are triggered

manually by a user in Lite.

With Pro, they are triggered automatically

when a threshold breach occurs.

Lite is limited to 2 hours of rolling data. Pro is unlimited and can hold years of

FALL 2013


historical data

NA Pro also monitors the Host OS machine to

provide visibility into CPU, memory, disk

and network utilization

NA Pro for Java has automatic memory leak

detection for resolving leaks in production

environments.

NA Pro offers workflow automation so that

proactive action can be taken when needed.

For example, AppDynamics enables cloud

bursting when more resources need to be

provisioned.

Table 6.1 AppDynamics Lite vs AppDynamics Pro

REFERENCES

[1]www.appdynamics.com

[2] http://litedocs.appdynamics.com

[3] http://www.reekya.pl/appd/images/downloads/ReekyaAppDynamics_Lite%20vs.%20Pro.pdf

FALL 2013


Performance Evaluation 7. Performance Evaluation

7.1. Deployment Architecture

The Test environment consisted of five machines viz, 4 Eucalyptus Community Cloud [1]

Instances and one local machine for the benchmark test. The Architecture was deployed in a

distributed manner with one instances dedicated to open source HaProxy load balancer , two

ECC instances with Apache Tomcat[2] server hosting the application files of konakart and a

single instance dedicated to the konakart MySql database which is shared by both the

konakart java applications hosted on the Tomcat servlet container. Apache JMeter[3]is used

to generate load on the Haproxy load balancer[4]. JMeter was configured on localhost to

simulate real world traffic over the public internet.

FIG 7.1 Deployment Architecture

Apache

JMeter

KONAKART-

TOMCAT1

HAPROXY

KONAKART-

TOMCAT2

Depl nt A hi

KONAKA

RT

MySql

Database

- KO

euca-173-205-188-102.eucalyptus.ecc.eucalyptus.com:8080/konakart

10.9.40.2:8080/konakart 10.9.40.1:8080/konakar

10.9.40.3:330 10.9.40.3:3306

Local terminal

ECC

ECC ECC

ECC

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The konakart application communicates with the konakart MySql database using the

MySql Java Database Connector (JDBC). The Haproxy load balances the traffic

between the servers using the private IP address of the servers and the port. As

Konakart is a ecommerce application, the database between the instance is shared to

maintain consistency between the transactions. Apache JMeter is configured to generate

HTTP request over a time period of 300 sec. The number of requests is varied until the

application breakdown. A IaaS level monitoring tool “collectd”[5] is deployed on all

the ECC instances, which collect IaaS level metrics like CPU usage, load , Disk

operations and memory.

7.2. Test Goals

The goals of the above performance evaluation tests are:

· To test performance of an E-commerce application on open source private cloud

infrastructure

· To Compare PaaS Level application metrics collected by the AppDynamics Lite

monitoring and IaaS level metrics collected by collectd tool.

· To analyze performance of the architecture and provide possible improvements in

the architecture.

7.3. Test Environment

All the Eucalyptus instances have following configuration

Instance Type m3.xlarge

No of Processors 4

RAM 2048 MB

Storage 15 GB

OS Ubuntu Lucid 10.04

Kernel Linux

Architecture x86_64 bit

Table 7.1 ECC Instance Configuration

7.4. Test Phase 1

Apache JMeter was configured to generate load of 500, 1000, 1500, 2000, 2500, 3000,3500

and 4000 HTTP requests.

7.4.1. PaaS Metric results(AppDynamics

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No of

HTTP

Requests

Server

Throughput

(no of calls

per minute)

Database

Throughput

(no of calls

per minute)

Haproxy

Average

Response

time(ms)

500 20 1333 500 Server 1

20 1334 500 Server 2

1000 42 1522 600 Server 1

43 1522 600 Server 2

1500 104 3076 500 Server 1

113 3333 500 Server 2

2000 145 4666 500 Server 1

140 4505 500 Server 2

2500 179 5747 500 Server 1

169 5824 500 Server 2

3000 217 6967 500 Server 1

200 6767 500 Server 2

3500 270 8666 500 Server 1

262 8404 500 Server 2

4000 291 9333 600 Server 1

291 9333 600 Server 2

Table 7.2 Apache Jmeter load testing results

Note: Each of the above throughput values are average values over last 30 minutes

Fig 7.2 No of HTTP Requests Vs Web server Throughput

0

50

100

150

200

250

300

350

0 1000 2000 3000 4000 5000

No Of HTTP Requests

Server 1 Throughput(calls/min)

Server2 Throughput(Calls/min)

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Fig 7.3 No of HTTP requests vs Database Server throughput

7.4.1.1. Observations(PaaS Metric results):

· As the Haproxy load balancing algorithm was set to “round-robin”, the load

balancer efficiently load balances the traffic between two web servers as the

throughput for both the web servers is almost equal.

· We were successful to distribute the traffic among web servers.

· The database throughput is very high, as single database is shared by both

the web servers.

· The load balancer response time was almost constant throughout the tests, so

we can say that, the deployment setup handled the web traffic efficiently.

· The main short-coming of AppDynamics Lite is that it aggregates the

application performance statistics over a 2 hour period.

7.4.2. IaaS Metric Results (collectd)

No of HTTP requests CPU USAGE LOAD

500 0.75 0.07 Server 1

0.92 0.08 Server 2

1000 1.53 0.14 Server 1

2.05 0.18 Server 2

1500 1.47 0.15 Server 1

1.63 0.14 Server 2

2000 1.54 0.21 Server 1

1.98 0.23 Server 2

2500 1.78 0.22 Server 1

1.75 0.27 Server 2

3000 2.08 0.24 Server 1

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 1000 2000 3000 4000 5000

No of HTTP Requests

Database throughput from

Server 1

Database throughput from

Server 2

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2.06 0.31 Server 2

3500 2.46 0.22 Server 1

2.44 0.34 Server 2

4000 2.8 0.28 Server 1

2.78 0.3 Server 2

Table 7.3 Apache Jmeter load testing results(IAAS metrics)

Fig 7.4. No of HTTP requests vs average CPU usage(jiffies)

Fig 7.5 No of HTTP requests Vs Average System Load

7.4.2.1. Observations(IaaS Metrics)

· The unit of CPU usage is Jiffies. A jiffy is the total duration of one tick of

the system timer interrupt. It is not an absolute time interval unit, since its

0

0.5

1

1.5

2

2.5

3

0 1000 2000 3000 4000 5000

J

I

F

F

I

E

S

No of HTTP requests

CPU usage webserver 1

CPU usage webserver 2

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 1000 2000 3000 4000 5000

S

y

s

t

e

m

L

o

a

d

No of HTTP requests

LOAD SERVER 1

LOAD SERVER 2

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duration depends on the clock interrupt frequency of the particular hardware

platform.[6] Every Hardware platform has a different jiffy value. A Jiffy is a

proverbial short amount of time, which is 1/100 second on most CPUs. The

collectd CPU plugin [7], collects the amount of time spent by the CPU in

various states, most notably executing user code, executing system code,

waiting for IO-operations and being idle in terms of ‘jiffies’. Linux Systems

provide cumulative count of no of jiffies spent in user, nice, system and idle

CPU modes over a specified period of time. In most Systems, 4 jiffies counts

are provided, viz the minimum value, average value, maximum value and last

value which a calculated over a specific period of time. In our case, we have

considered the average value of user CPU mode over a one hour period. By

observing the trend of CPU utilization (Fig 7.4), we can conclude that no of

Jiffies increases as we increase no of HTTP requests.

· System load average represents the average number of processes that are in

the running (using the CPU) or runnable (waiting) states. One notable

exception exists: Linux includes processes in uninterruptible sleep states,

typically waiting for some I/O activity to complete. This can markedly

increase the load average on Linux systems. The load average is calculated as

an exponential moving average of the load number (the number of processes

that are running or runnable). The three numbers returned as the system’s load

average represent the one, five, and fifteen minute moving load average of the

system. for a single processor machine a load average of 1 means that, on

average, there is always a process in the running or runnable state. Thus, the

CPU is being utilized 100% of the time and is at capacity. In our case we the

machines we selected have 4 cpu’s , so for 100 % utilization , the value of

load average should be 4. By observing the trend of system load (Fig 7.5)

(Table 7.2)over increasing amount of HTTP requests, we can conclude that

the system load increases with no of HTTP requests. The highest value

attained by Load is 0.3. hence the system is very High –End as the maximum

value of System load average for 100 % cpu utilization is 4(4 cores). We can

compromise the system configuration to lower the cost over a public cloud

infrastructure.

7.5. Testing Phase 2

In this phase we will closely monitor the performance of the database server as two web

server instances share same database and throughput of the database server is more as

compared to the individual web server throughput. In this phase , we analyze the IaaS level

metrics for the database server.

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No Of HTTP

requests

CPU Usage LOAD

1000 2.18 0.16 Server 1

2.42 0.17 Server 2

0.12 0.05 Database

Server

4000 2.91 0.22 Server 1

2.73 0.26 Server 2

0.81 0.09 Database

Server

Table 7.4 Apache Jmeter Load Testing results Phase 2

· By observing the IaaS metrics from above table we can conclude that the total

no of database calls does not affect the server performance at the IaaS level. The

actual load on the database server cannot be presented in terms of IaaS metrics

like CPU usage and LOAD.

7.6. Testing Phase 3

In this phase we closely monitor the mysql database performance using the MySQL plugin

[8]. The MySQL plugin connects to an MySQL-database and issues a SHOW

STATUS command periodically. The command returns the server status variables, many of

which are collected [8].This plugin will help to find the performance statistics of

KONAKART-MySQL database and display it in a graphical manner.

7.6.1. Phase 3 Results:

Fig 7.6 MySql Plugin Metric.

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No of HTTP requests MysqlCommands(Select

statements)per second

500 6.24

1000 12.16

4000 36.58

Table 7.5 No of MySql Commands per second

· Above figure (Fig 7.6) contain the number of issues per second for various

SQL-commands. But in the case of static testing we will only analyze the Mysql

“Select statement”.

· For 500 requests, the average value of MySqlcommands(Select) is 6.24 , that

means 6.24 select commands were executed in one second. as we run the test for

300 seconds the approximate number of select requests will be 1872.

· Similarly in the case of 1000 and 4000 requests the approximate no of Select

requests will be 3648 and 10,974 respectively.

· This implies that there are high no of requests on the database server and we

need to dig deep into MySql performance statistics as the traditional IaaS

metrics does not provide enough information about the actual load on the

database server.

· In our case, we only analyzed one MySql command, but in production

environment there are many MySql commands executed simultaneously, which

will add more load to the Database server. Hence , Auto-scaling the database is

must in such scenarios.

7.7. Comparing IaaS performance metrics with PaaS performance metrics:

· Load

System load at the IaaS level gives a rough overview of utilization of the machine,

The system load is defined as the number of runnable tasks in the run-queue and is

provided by many operating systems as a one, five or fifteen minute average [5]. The

PaaS level metric Load, monitored by AppDynamics, is the application level load

and defines more about the throughput of a single JVM. By observing collectd

statistics, we can deduce that the IaaS level load increases for both the web servers

as we increase the total no of HTTP requests. But in case of database server the load

plugin of collectd does not give enough information about the actual load as the

AppDynamics Load metric (Throughput) is high for Database server.

· Response Time:

The AppyDynamics Lite response time metric corresponds to the time taken by the

web servers to respond to Haproxy load balancer. Whereas the Response time in

case of the Haproxy load balancer (Apache Jmeter metric) is the time taken by the

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load balancer to respond to a HTTP request , which can be a sum of web server

response time and time taken for a request to reach Haproxy website from a local

Jmeter machine setup.

· CPU-utilization:

By observing the CPU metric on collectd, we can conclude that the number of

system Input/output waits increase as we increase the no of HTTP requests.

REFERENCES

[1] http://www.eucalyptus.com/eucalyptus-cloud/get-started/try/community-cloud

[2] http://tomcat.apache.org/

[3] http://jmeter.apache.org/

[4] http://haproxy.1wt.eu/

[5]https://collectd.org/wiki/index.php/Plugin:Load

[6] http://en.wikipedia.org/wiki/Jiffy_(time)

[7] https://collectd.org/wiki/index.php/Plugin:CPU

[8] https://collectd.org/wiki/index.php/Plugin:MySQL

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Conclusion and Future Work 8. Conclusion and Future Work

8.1. Conclusion

The Problem on evaluating an open

source e-commerce framework developed over a cloud infrastructure is presented in this

project deployed as a multi-tier architecture. More specifically, this project focuses on

challenges faced in improving the application performance running over multi-tenant cloud

platforms. This project evaluates AppDynamics Lite as a possible PaaS level solution for

application monitoring in cloud infrastructures.

E-commerce websites are emerging very rapidly in recent years. With likes of major e-

commerce players such as Amazon.com,ebay.com, etc, in the market , many small vendors

are emerging to improve their sales and so that they can reach customers from all corners of

the world. An E-commerce website must handle most critical business transactions or

operations efficiently. E-commerce, being a service oriented business should meet all the

consumer demand, hence monitoring such website is important. The most important rule

that businesses follow nowadays is to increase the profits and reduce losses, hence they

prefer deploying their websites in a multi-tenant architecture, which are cheap as multiple

organizations or vendors share computing resources. As public web-hosting service or

public clouds provide multi-tenant hosting services for very affordable prices, monitoring

and auto-scaling has become very essential for such public cloud. AppDynamics provide

such monitoring solution to monitor application level metrics.

As AppDynamics Lite version collects monitoring data for over a period of 2 hours, so data

analysis is impossible in this case but the AppDynamics Pro version doesn’t have this

restriction and also has more advanced features.

From the test results it can be concluded that Konakart E-commerce is a database intensive

application as number of transactions between web servers and the database server is greater

than the no of HTTP requests. During High traffic and load conditions, the shared database

server might breakdown. Hence a scaling of database server is more important in the case of

e-commerce frameworks.

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8.2. Future Work

Future Work on developing a open

source ecommerce platform can include evaluating different multi-tier deployment

archictecture of konkart on Eucalyptus. Finding a solution for how to scale database servers

will be good step forward for such e-commerce applications. Evaluating e-commerce

solution on various other cloud infrastructures such as Amazon AWS and to compare the

performances of the application on these clouds. Also evaluating various open source e-

commerce applications like magento , Zen Cart , osCommerce, openCart, spree commerce,

prestashop, etc. over various cloud infrastructures can be regarded as future research work