Cloud Computing

ECE7650

Cloud Computing Case Study

At one extreme, sharing basic IT infrastructure E.g Amazon’s EC2: an EC2 instance appears physical

HW, users can control nearly the entire sw stack, from the kernel upwards

At the other extreme, sharing application domain-specific platforms E.g. Google AppEngine, a platform for building and

hosting web applications on Google Web servers. Limited range of applications are supported,

In the middle, sharing programming environments Microsoft’s Azure written using .NET libraries, and

compiled to the Common Langurage Runtime (CLR).

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Amazon Web Services AWS is a collection of remote computing services that together

make up a cloud computing platform, offered over the Internet by Amazon.com.

List of AWS products Amazon Elastic Compute Cloud (EC2) provides scalable virtual

private servers using Xen. Amazon Simple Storage Service (S3) provides Web Service based

storage. Amazon SimpleDB allows developers to run queries on structured

data. It provide "the core functionality of a database." Amazon Elastic MapReduce allows developers to easily and cheaply

process vast amounts of data. It uses a hosted Hadoop framework running on the web-scale infrastructure of EC2 and Amazon S3.

Amazon Virtual Private Cloud (VPC) creates a logically isolated set of Amazon EC2 instances which can be connected to an existing network using a VPN connection.

And More…

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Amazon’s EC2

Amazon Elastic Compute Cloud (EC2) Web service that provides resizable compute

capacity in the cloud

An EC2 instance appears physical HW, provides users complete control over nearly entire sw stack, from the kernel upwards Load Variety of operating system Install Custom applications Manage network access permission Run image using as many/few systems as you desire

C. Xu @ Wayne State Cloud Computing 4

Amazon’s EC2 features Elastic capacity

Elastic resource config/reconfig; Elastic num of instances

Completely Control Root access/access to console output/data store/ reboot

Reliable Multiple locations Elastic IP addresses

Secure Firewall config Virtual Private Cloud

Performance Auto Scaling Auto local balancing

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Amazon’s EC2 Instances

On-Demand Instances Pay for capacity without long-term commitment

Reserved Instances Standard Instances Micro Instances High-Memory Instances High-CPU Instances High-I/O instance High Storage Instances

Spot Instances Bit on unused Amazon EC2 capacity, run those instances

for as long as their bid exceeds the current Spot Prices

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Amazon’s EC2 Usages Create an Amazon Machine Image (AMI) containing your

applications, libraries, data and associated configuration settings. Or use pre-configured, templated images to get up and running immediately.

Choose the types of instances and OS, then start, terminate, and monitor as many instances of your AMI as needed, using the web service APIs or the variety of management tools provided.

Determine whether you want to run in multiple locations, utilize static IP endpoints, or attach persistent block storage to your instances.

Pay only for the resources that you actually consume, like instance-hours or data transfer.

http://www.youtube.com/watch?v=JPFoDnjR8e8

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Amazon’s S3 Amazon Simple Storage Service (S3)

Storage for the Internet.

Features Unlimited Storage Highly scalable

• in terms of storage, request rate and concurrent users

Reliable• Store redundant data in multiple facilities and on multiple devices

Secure• Flexibility to control who/how/when/where to access the data

Performance• Choose region to optimize for latency/minimize costs

Work with other AWS products EC2/Elastic MR/Amazon Import/Export…

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Example : online photo processing service Photo operation

red eye reduction/cropping/customization/re-coloring/teeth whitening, etc

Procedure Web server receive request Put request message in the queue Pictures stored in S3 Multiple EC2 instances run photo processing Put back in the queue Return

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Google’s AppEngine A platform for building and hosting web

applications on Google data servers. support Python/Java Upload and ready to run No need to maintain your own servers, easy to scale

as your traffic and storage grow

Kevin Gibbs, App Engine Tech Lead “AppEngine is a system that exposes various pieces of Google’s scalable infrastructure so that you can write server-side applications on top”

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AppEngine Does One Thing Well AppEngine handles HTTP(S) requests, nothing else

Think RPC: request in, processing, response out Dynamic web serving, with full support for common web

technologies

App configuration is dead simple No performance tuning needed

Everything is built to scale “infinite” number of apps, requests/sec, storage capacity Automatic scaling

Automatic management Load balancing/monitoring/scaling

Local development Environment: simulation Google AppEngine Software Development Kit

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AppEngine Service URLFetch

fetch web resources/services Images

manipulate images: resize, rotate, flip, crop Google Accounts

Allow users to sign in their product by using G account Mail

Send message using Google infrastructure. XMPP

send instant messages Datastore

managing data objects

Memcache Distributed in-memory data cache in front of constant storage

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App Enginer Sandbox Appl run in a secure environment that

provides limited access to the underlying OS. Allow AppEngine to distribute web requests

for the application across multiple servers; Start and stop servers to meet traffic demands

Isolates your appl in its own secure, reliable environment that is independent of the hw, OS, and physical location of the web server

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App Enginer Sandbox Limited Access:

Appl can only access other computers on the Internet through provided URL fetch and email API

Cannot write to file system, can read file uploaded by the appl; The app must use the App Engine datastore for all data that persists between requests

Run in response to a web request, and must return response data within 30 seconds. A request handler cannot spawn a sub-process or execute code afte the response has been sent.

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App Engine Architecture (python)

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PythonVM

process

stdlib

app

memcachedatastore

mail

images

urlfech

statefulAPIs

stateless APIsreq/resp

App Engine Architecture

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Why Not LAMP? Linux, Apache, MySQL/PostgreSQL,

Python/Perl/PHP/Ruby LAMP is the industry standard But management is a hassle:

Configuration, tuning Backup and recovery, disk space management Hardware failures, system crashes Software updates, security patches Log rotation, cron jobs, and much more

More Important…

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Automatic Scaling Low-usage apps: many apps per physical host High-usage apps: multiple physical hosts per

app

Stateless APIs are trivial to replicate

Datastore built on top of Bigtable; designed to scale well Abstraction on top of Bigtable

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Automatic Scaling You don’t need to configure your resource needs One CPU can handle many requests per second Apps are hashed onto CPUs:

One process per app, many apps per CPU Creating a new process is a matter of cloning a generic

“model” process and then loading the application code (in fact the clones are pre-created and sit in a queue)

The process hangs around to handle more requests (reuse) Eventually old processes are killed (recycle)

Busy apps (many QPS query per sec) get assigned to multiple CPUs This automatically adapts to the need

• as long as CPUs are available

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AppEngine’s Resource Limit

Quota Limit

Apps per developer 10

Time per request 30 sec

Files per app 1,000

HTTP response size 10 MB

Datastore item size 1 MB

Appl code size 150 MB

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QuotaFree Quota

Emails per day 2,000

BW in per day 10,000 MB

BW out per day 10,000 MB

Secure BW in per day 2,000 MB

Secure BW out per day 2,000 MB

CPU megacycles per day 200,000,000

HTTP Requests per Day 1,333,328*

Datastore API calls per day 10,368,000*

Data stored 1 GB

URLFetch API calls per day 657,084*

AppEngine Development Demo http://www.youtube.com/watch?v=tcbp

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Microsoft’s Azure Cloud service platform hosted in MS data

centers, including an OS, Windows Azure, and a set of developer services like .NET services, Live services, and SQL data services Window Azure: serving as the development, service

hosting, data storage and service management environment

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Components of Azure

Compute ， Storage ， Fabric Controller ， Content Delivery Network (CDN) ， Connect

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Compute

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Web role: web-based application Worker role: various windows-based code VM role: Windows Server image, customized software

installations on OS will be kept.

Compute Submit an application with configuration of

type and number of instances Fabric controller creates VM and runs the code Requests through HTTP/HTTPS/TCP are load

balanced across all instances without affinity. Exposed API that allows

automatically scale up and down. application logging Monitoring resource usage: CPU/storage

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Storage: Blob, Table and Queue

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Blob Simple unstructured hierarchy: each container holds one or more blobs,

which is up to terabyte Access from URI:

http://<StorageAccount>.queue.core.windows.net/<QueueName> Basic storage

Storage: Blob, Table and Queue

Tables Structured storage. Stored in a group of entities that contain properties. Entity with different num of properties in various types. Not relational

tables, supportmassively scalable applications.

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Storage: Blob, Table and Queue Queues

Provide a way for communication between instances

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Three replications for all kinds

Fabric Controller

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All Azure application and data reside in Microsoft data center

All Azure components are managed by the fabric controller.

Fabric Controller Distributed application that’s replicated

across machines. It owns all of the resources: computer,

switches, load balancers and is aware of each azure application

Autonomic management Monitoring the system status Optimizing hardware utilization Create VMs and intelligent deployment Update instances without shutdown Managing the OS for Web and Work roles

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Content Delivery Network

The CDN stores copies of a blob at sites closer to the clients that use it

Servers store videos, Flash, HTML5…

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Connect Effectively connecting on-premises environments with Windows Azure Installing a Endpoint Agent on each on-premises machines and

communication through IP-level connection Azure applications appears to be on the same IP network as the on-

premises machines.

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Case Study 1 Large scale web applications with occasional huge spikes and

background processing. Video sharing site

Deployment A number of web instances

based on demand Table storage

for information Many works for processing Blobs storage for

large data set

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Case Study 2 Parallel processing applications

Financial modeling at a bank New drug testing simulations in a pharmaceutical company

Deployment Web role for access interface Many workers

for processing Large data set stored in blobs

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Case Study 3 Using storage from an on-premises or hosted application

Archive old email User log file

Deployment Connect on-premises

application with Azure

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Case Study

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