Improve Energy Efficiency Model for Cloud Computing

International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169

Volume: 3 Issue: 9 5500 - 5504

______________________________________________________________________________________

5500

IJRITCC | September 2015, Available @ http://www.ijritcc.org

_______________________________________________________________________________________

Improve Energy Efficiency Model for Cloud Computing

Dhanraj Meena1, Dr. R.K.Gupta

2, Mahesh Verma

3

1M.Tech Scholar, Gyan Vihar University, Jaipur, Rajasthan, India

2 Professor, E.C.E Deptt., Gyan Vihar University, Jaipur, Rajasthan, India

3M.Tech Scholar, Gyan Vihar University, Jaipur, Rajasthan, India

Abstract:- Cloud computing is an “evolving paradigm” that has redefined the way Information Technology based services can be offered. It has

changed the model of storing and managing data for scalable, real time, internet based applications and resources satisfying end users’ needs.

More and more remote host machines are built for cloud services causing more power dissipation and energy consumption. Over the decades,

power consumption has become an important cost factor for computing resources. In this thesis we will investigate all possible areas in a typical

cloud infrastructure that are responsible for substantial amount of energy consumption and we will address the methodologies by which power

utilization can be decreased without compromising Quality of Services (QoS) and overall performance. We also plan to define the scope for

further extension of research from the findings we would have from this thesis . In this thesis we are using energy aware rate monotonic

scheduling for improve the performance of packet lost . Packet lost are reducing by the proposed algorithm.

Keywords: Cloud computing, energy efficiency, scheduling, cluster.

__________________________________________________*****_________________________________________________

1. INTRODUCTION

The latest innovations in cloud computing are making our

business applications even more mobile and collaborative,

similar to popular consumer apps like Facebook and Twitter.

As consumers, we now expect that the information we care

about will be pushed to us in real time, and business

applications in the cloud are heading in that direction as

well.

Cloud computing models are shifting. In the cloud/client

architecture, the client is a rich application running on an

Internet-connected device, and the server is a set of

application services hosted in an increasingly elastically

scalable cloud computing platform. The cloud is the control

point and system or record and applications can span

multiple client devices. The client environment may be a

native application or browser-based; the increasing power of

the browser is available to many client devices, mobile and

desktop alike.

Robust capabilities in many mobile devices, the increased

demand on networks, the cost of networks and the need to

manage bandwidth use creates incentives, in some cases, to

minimize the cloud application computing and storage

footprint, and to exploit the intelligence and storage of the

client device. However, the increasingly complex demands

of mobile users will drive apps to demand increasing

amounts of server-side computing and storage capacity.

1.1 CLOUD COMPUTING AN OVERVIEW

Cloud computing is a computing paradigm, where a large

pool of systems are connected in private or public networks,

to provide dynamically scalable infrastructure for

application, data and file storage. With the advent of this

technology, the cost of computation, application hosting,

content storage and delivery is reduced significantly.

Cloud computing is a practical approach to experience direct

cost benefits and it has the potential to transform a data

center from a capital-intensive set up to a variable priced

environment.

The idea of cloud computing is based on a very fundamental

principal of „reusability of IT capabilities'. The difference

that cloud computing brings compared to traditional

concepts of “grid computing”, “distributed computing”,

“utility computing”, or “autonomic computing” is to

broaden horizons across organizational boundaries.

Forrester defines cloud computing as:

“A pool of abstracted, highly scalable, and managed

compute infrastructure capable of hosting end-customer

applications and billed by consumption.”

Fig 1: Conceptual View of Cloud Computing

http://www.ijritcc.org/


Volume: 3 Issue: 9 5500 - 5504

______________________________________________________________________________________

5501


_______________________________________________________________________________________

1.2 CLOUD COMPUTING MODELS

1. Software as a Service (SaaS): In this model, a

complete application is offered to the customer, as a

service on demand. A single instance of the service runs

on the cloud & multiple end users are serviced. On the

customers‟ side, there is no need for upfront investment

in servers or software licenses, while for the provider,

the costs are lowered, since only a single application

needs to be hosted & maintained. Today SaaS is offered

by companies such as Google, Salesforce, Microsoft,

Zoho, etc.

2. Platform as a Service (Paas): Here, a layer of

software, or development environment is encapsulated

& offered as a service, upon which other higher levels

of service can be built. The customer has the freedom to

build his own applications, which run on the provider’s

infrastructure. To meet manageability and scalability

requirements of the applications, PaaS providers offer a

predefined combination of OS and application servers,

such as LAMP platform (Linux, Apache, MySql and

PHP), restricted J2EE, Ruby etc. Google‟s App Engine,

Force.com, etc are some of the popular PaaS examples.

3. Infrastructure as a Service (Iaas): IaaS provides

basic storage and computing capabilities as

standardized services over the network. Servers, storage

systems, networking equipment, data centre space etc.

are pooled and made available to handle workloads.

The customer would typically deploy his own software

on the infrastructure. Some common examples are

Amazon, GoGrid, 3 Tera, etc.

Fig 2: Cloud Model

1.3 UNDERSTANDING PUBLIC AND PRIVATE

CLOUDS:-

Enterprises can choose to deploy applications on Public,

Private or Hybrid clouds. Cloud Integrators can play a vital

part in determining the right cloud path for each

organization.

Public Cloud

Public clouds are owned and operated by third parties; they

deliver superior economies of scale to customers, as the

infrastructure costs are spread among a mix of users, giving

each individual client an attractive low-cost, “Pay-as-you-

go” model. All customers share the same infrastructure pool

with limited configuration, security protections, and

availability variances. These are managed and supported by

the cloud provider. One of the advantages of a Public cloud

is that they may be larger than an enterprises cloud, thus

providing the ability to scale seamlessly, on demand.

Private Cloud

Private clouds are built exclusively for a single enterprise.

They aim to address concerns on data security and offer

greater control, which is typically lacking in a public cloud.

There are two variations to a private cloud.

On-premise Private Cloud

On-premise private clouds, also known as internal clouds

are hosted within one‟s own data center. This model

provides a more standardized process and protection, but is

limited in aspects of size and scalability. IT departments

would also need to incur the capital and operational costs for

the physical resources. This is best suited for applications

which require complete control and configurability of the

infrastructure and security.

Externally hosted Private Cloud

This type of private cloud is hosted externally with a cloud

provider, where the provider facilitates an exclusive cloud

environment with full guarantee of privacy. This is best

suited for enterprises that don‟t prefer a public cloud due to

sharing of physical resources.

Hybrid Cloud

Hybrid Clouds combine both public and private cloud

models. With a Hybrid Cloud, service providers can utilize

3rd party Cloud Providers in a full or partial manner thus

increasing the flexibility of computing. The Hybrid cloud

environment is capable of providing on-demand, externally

provisioned scale. The ability to augment a private cloud

with the resources of a public cloud can be used to manage

any unexpected surges in workload.

2. PROBLEM STATEMENT

Cloud Computing has emerged as a new consumption and

virtualization model for the high cost computing

infrastructures and web based IT solutions. Cloud provides

suitable, on-demand service, elasticity, broad network

access, resource pooling and measured service [1] in highly

customizable manner with minimal management effort. The

application of low-cost computing devices, high-

performance network resources, huge storage capacity,

semantic web technology, SOA (Service Oriented

Architecture), usage of API (Application Programming

Interfaces), etc., have helped in the swift growth of cloud



Volume: 3 Issue: 9 5500 - 5504

______________________________________________________________________________________

5502


_______________________________________________________________________________________

technology. A cloud infrastructure generally encapsulates all

those existing technologies in a web service based model to

offer business agility, improved scalability and on demand

availability. The rapid deployment model, low start up

investment, pay-as-you-go scheme, multi-tenant sharing of

resources are all added attributes of cloud technology due to

which major industries tend to virtualization for their

enterprise applications [2].

Cloud applications are deployed in remote data centers

(DCs) where high capacity servers and storage systems are

located. A fast growth of demand for cloud based services

results into establishment of enormous data centers

consuming high amount of electrical power. Energy

efficient model is required for complete infrastructure to

reduce functional costs while maintaining vital Quality of

Service (QoS). Energy optimization can be achieved by

combining resources as per the current utilization, efficient

virtual network topologies and thermal status of computing

hardwares and nodes. On the other hand, the primary

motivation of cloud computing is related to its flexibility of

resources. As more and more mobile devices are getting

considered as major consumption points for remote users in

mainstream business, power management has been a

bottleneck for proper functioning of services at users end. A

trade-off between energy consumed in computation and the

same in communication has been the critical aspect to be

considered for mobile clients also.

In this paper we plan to consolidate all the plausible aspects

of energy efficient infrastructure model for cloud data

centers while considering performance bottlenecks for the

same.

2.1. Energy Consumption Analysis

To calculate the amount of energy consumed by data

centers, two metrics were established by Green Grid, an

international consortium [10]. The metrics are Power Usage

Effectiveness (PUE) and Data Centre Infrastructure

Efficiency (DCiE) as defined below:

PUE = Total Facility Power/IT Equipment Power

DCiE = 1/PUE = (IT Equipment Power/Total Facility

Power) x 100%

The IT equipment power is the load delivered to all

computing hardware resources, while the total facility power

includes other energy facilities, specifically, the energy

consumed by everything that supports IT equipment load.

In cloud infrastructure, a node refers to general multicore

server along with its parallel processing units, network

topology, power supply unit and storage capacity. The

overall energy consumption of a cloud environment can be

classified as follows [9]:

ECloud = ENode + ESwitch + EStorage + EOhters

Consumption of energy in a cloud environment having n

number of nodes and m number of switching elements can

be expressed as:

ECloud = n (ECPU + EMemory + EDisk + EMainboard + ENIC) +

m(EChassis + ELinecards + EPorts ) + (ENASServer + EStorageController +

EDiskArray) + EOthers

2.2. ENERGY EFFICIENCY IN CLOUD

INFRASTRUCTURES

Building an energy efficient cloud model does not indicate

only energy efficient host machines. Other existing

components of a complete cloud infrastructure should also

be considered for energy aware applications. Several

research works have been carried out to build energy

efficient cloud components individually. In this section we

will investigate the areas of a typical cloud setup that are

responsible for considerable amount of power dissipation

and we will consolidate the possible approaches to fix the

issues considering energy consumption as a part of the cost

functions to be applied.

2. 3. ENERGY EFFICIENT HARDWARE

One of the best approaches to reduce the power

consumption at data centre and virtual machine level is

usage of energy efficient hardwares at host side.

International standard bodies such as: European TCO

Certification [3], US Energy Star [4] are there to rate energy

efficient consumer products. The rating is essential to

measure the environmental impact and carbon footprint of

computer products and peripherals.

2.4 MEMORY-AWARE SCHEDULING IN

MULTIPROCESSOR SYSTEMS

Main issue from the memory aware scheduling is high

packet lost and low Residual energy. In present multi core

systems, cores on the chip share resources such as caches,

DRAM etc. Tasks running on one core may harmfully affect

the performance of tasks on other cores and hence it may

even maliciously create a Denial of Service (DoS) attack on

the same chip [17]. Task assortment should be optimized by

co-scheduling them in the processor cores considering

memory contention and frequency selection.

Memory aware task scheduling is based on run queue

sorting followed by frequency selection [16]. Run queue

sorting is a time slice based multiprocessor scheduling



Volume: 3 Issue: 9 5500 - 5504

______________________________________________________________________________________

5503


_______________________________________________________________________________________

algorithm which is a specific form of gang scheduling. For

further avoidance of memory contention, frequency

selection can be used which allows processor switching to a

suitable frequencies for each task without causing any

significant performance overhead.

First, all tasks are sorted in descending order based on their

execution time. Tasks with lower execution time are more

flexible when it comes to their scheduling as their impact on

the critical path is not as great as tasks with long execution

times. We then proceed to build our schedule one task at a

time. We get the next available CPU p and task t from the

ordered task list and check to see if all of its dependencies

are done executing. If t’s dependencies have not completed

their execution, we check to see if there are early execution

edges for t. For every early execution edge going to t, we

uses the edge’s information to determine if it is possible to

execute the task t at the current time without having to wait

for its dependence to complete its execution. We verify that

all dependencies meet the early execution criteria by looking

at their current loop’s iterator/iteration pair. If these match

the iterator/iteration pair in the early execution edge, we can

assume that we can start the execution of t. Before we map

task t, we look at the data currently placed in p’s SPM, and

search for a task alt which depends on this data. If we find

such task, we map it to p, otherwise, we map task t to p at

the cost of a DMA transfer. This helps us keep the number

of DMA transfer to a minimum.

3. PROPOSED METHODOLOGY

We are introducing energy aware rate monotonic scheduling

for reduce the packet loses during the data uploading and

downloading . According to the existing design algo

(Memory aware scheduling ) the packet losses are too much.

4. RESULTS

In figure 3 the images shows that which we have to

download and upload .

Fig 3:- Image for uploading

In figure 4 energy efficient for the memory aware

scheduling and energy aware rate monotonic scheduling .

Fig 4:- Energy efficient v/s number of nodes

In figure 5 graph is showing the packet loss for number of

nodes for data upload and download .

Fig 5:- Packet loss vs number of nodes



Volume: 3 Issue: 9 5500 - 5504

______________________________________________________________________________________

5504


_______________________________________________________________________________________

In figure 5 graph is showing the packet loss for time for data

upload and download .

Fig6:- packet loss vs time

CONCLUSION

In this paper we have investigated the need of power

consumption and energy efficiency in cloud computing

model. It has been shown that there are few major

components of cloud architecture which are responsible for

high amount of power dissipation in cloud. The possible

ways to meet each sector for designing an energy efficiency

model has also been studied. Finally we have shown the

future research direction and the continuity of this work for

next level implementation.

References

[1] P. Mell and T. Grance, “The NIST definition of

cloud computing”, National Institute of Standards

and Technology, vol. 53, no. 6, (2009).

[2] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R.

Katz, A. Konwinski, G. Lee, D. Patterson, A.

Rabkin, I. Stoica and M. Zaharia, “Above the

Clouds: A Berkeley View of Cloud Computing”,

Tech. rep., (2009) February, UC Berkeley Reliable

Adaptive Distributed Systems Laboratory.

[3] European TCO Certification,

http://www.tcodevelopment.com.

[4] Energy Star, http://www.energystar.gov,

http://www.euenergystar.org.

[5] Intel whitepaper “Wireless Intel SpeedStep Power

Manager: optimizing power consumption for the

Intel PXA27x processor family”.

[6] “AMD PowerNow!™ Technology: dynamically

manages power and performance”, Informational

white paper.


Documents

Improve Energy Efficiency Model for Cloud Computing