java tech day 2009 - developing cloud computing applications with java

Developing Cloud Computing Applications with Java

Shlomo SwidlerCTO, [email protected]

Developing Cloud Computing Applications with

Java Overview of Cloud Computing

Amazon’s Cloud Platform

Google’s Cloud Platform

Application Development Challenges Posed by the Cloud…… and Java Solutions

222 June 2009 Developing Cloud Computing Applications with Java by Shlomo Swidler

About Me

CTO & co-Founder Music marketing on social networks Patent-pending targeting technology Java, MySQL, auto-scaling & cloud-based

Active in the Cloud Computing community Open Cloud Computing Interface Working Group

(an Open Grid Forum initiative) participant Contributor to Open Source cloud & Java projects

22 June 2009 3Developing Cloud Computing Applications with Java by Shlomo Swidler

Cloud Computing Is…

A style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure in the “cloud” that supports them.

– Wikipedia



A style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure in the “cloud” that supports them.

– Wikipedia



Computing Resources

As a Service Pay-as-you-go or Subscription


Infrastructure Platform Software

Processor LAMP Stack Email

Memory JVM CRM System

Storage Python VM ERP System

Network MapReduce SCM System

Advantages ofCloud Computing

From a Developer’s Perspective:

Pay-as-you-go “utility computing” Saves time Saves $$$

On-demand resource allocation & release

Scalability More on this later


Risks ofCloud Computing

Security Who else has access to “your” resources ?

Recovery How easy is it ?

Provider lock-in


Amazon’s Cloud Platform:Amazon Web Services

Infrastructure-as-a-Service

Processors & Memory Elastic Compute Cloud

“EC2”

Storage Simple Storage Service

“S3” Elastic Block Store

“EBS” SimpleDB database


Network Content Delivery

Network CloudFront

Messaging Simple Queue Service

“SQS”

Amazon DashboardElasticFox Firefox plugin


Developing onAmazon’s Cloud

Standard stuff:

Language

Libraries

Communications

Web Servers

Application Servers

Databases


Challenges:

Scaling

Suitable for existing applications

Google’s Cloud Platform:Google App Engine


Platform-as-a-Service

Language Python Java

Storage JDO or JPA or Datastore

APIs

User Accounts

Email

Image Transformation

Memcached

Cron jobs

Google DashboardGoogle Administration Console

22 June 2009 Developing Cloud Computing Applications with Java by Shlomo Swidler 15

Developing onGoogle’s Cloud

Easy stuff:

Scaling


Challenges:

Language

Libraries

Communications

Data Storage

Suitable for new, lighter-weight applications

Application Development Challenges Posed by the

Cloud Deploying to the Cloud

Designing for Scalability Web Tier Application Tier Database Tier


Deploying to the Cloud

IaaS platforms Mostly the same as traditional deployment

PaaS & SaaS platforms Custom procedures Custom configurations Custom tools

Google App Engine: Eclipse plug-in


Deploying an Application toGoogle App Engine


Designing the Application Tier for Scalability


Designing the Application Tier for Scalability

Make sure your Storage Tier is optimized Optimize database queries Use in-memory caching

Parallelize operations Use concurrent threads Use the Service Pools design pattern


Parallelize withConcurrent Threads

Motivation Allow long-running tasks to proceed without

impacting performance

Java offers the java.util.concurrent package Executor interface Future<T> interface


java.util.concurrent Example: In-Memory Cache Existing implementations such as memcached

Cache shared by all application instances Access is via the network

Application requests an Object from the cache

Time until response is received can vary True of any network operation

Don’t let application code wait…


java.util.concurrent

Exam

ple

: In-M

em

ory

C

ach

eString userId = "visitor01";

String memcachedKey = "userId:" + userId + ".lastLoginDate";

Future<Date> lastLoginDateGetter = MemcachedClient.get(memcachedKey, Date.class);

// perform the rest of the request handling code here

// then, at the end, get the user's last login date

Date lastLoginDate = null;

try {

lastLoginDate = lastLoginDateGetter.get(50, TimeUnit.MILLISECONDS);

} catch (InterruptedException e) {

// someone interrupted the FutureTask

} catch (ExecutionException e) {

// the FutureTask threw an exception

} catch (TimeoutException e) {

// the FutureTask didn't complete within the 50ms time limit

lastLoginDateGetter.cancel(false);

}

// return lastLoginDate to the presentation layer


java.util.concurrent

Exam

ple

: In-M

em

ory

C

ach

e

import java.util.concurrent.Callable;

import java.util.concurrent.Executor;

import java.util.concurrent.Executors;

import java.util.concurrent.Future;

import java.util.concurrent.FutureTask;

public class MemcachedClient {

private static Executor executor = Executors.newFixedThreadPool(1);

public static <T> Future<T> get(String objectKey, Class<T> type) {

final String objectKeyFinal = objectKey;

FutureTask<T> getFromCacheOperation = new FutureTask<T>(

new Callable<T>() {

public T call() {

Object networkResponse = requestObjectOverNetwork(objectKeyFinal);

return (T) networkResponse;

}

}

);

executor.execute(getFromCacheOperation);

return getFromCacheOperation;

}

private static Object requestObjectOverNetwork(String objectKey) {

// network stuff goes in here

}

}


Parallelize with Service Pools

Motivation Allow services to scale according to demand Scale up and down


Request Queue

Response Queue

Storage

Java Service Pool for Amazon Web Services:

Lifeguard Open source Apache License Version 2.0 http://code.google.com/p/lifeguard/


Request Queue

Response Queue

Storage

Lifeguard Framework Framework provides:

Message handling File handling Scaling logic


Request SQS Queue

Response SQS Queue

S3 Storage

EC

2

Insta

nce

s

Ingestor

ListenerPool Mgr

Config

Lifeguard Framework You provide:

Ingestor Service Pool Manager Configuration


Request SQS Queue

Response SQS Queue

S3 Storage

EC

2

Insta

nce

s

Ingestor

ListenerPool Mgr

Config


public class ResizeImageIngestor extends IngestorBase {

private static final String ResizeImageWorkflowXML ="<Workflow>" +

"<Service>" +"<Name>ResizeImage</Name>" +"<WorkQueue>ResizeImage-input</WorkQueue>" +

"</Service>" +"</Workflow>";

public ResizeImageIngestor() {

super(ResizeImageIngestorWorkflowXML);

}

public void ingest(File imageFile) {

super.ingest(Collections.singletonList(imageFile));

}

}Ingestor

S3 Storage

Imple

ment th

e In

gesto

r


S3 Storage

Imple

ment th

e S

erv

icepublic class ResizeImageService extends AbstractBaseService {

private static final String ServiceConfigXML ="<ServiceConfig>" +

"<ServiceName>ResizeImage</ServiceName>" +"<WorkQueue>ResizeImage-input</WorkQueue>" +

"</ServiceConfig>";

public ResizeImageService() {super(ServiceConfigXML);

}

public List<File> executeService(File imageFile) {

Image origImage = new Image(imageFile);

File resizedImageFile = resizeImageToFile(origImage);

return Collections.singletonList(resizedImageFile);

}

}


Con

fig

ure

the P

ool M

an

ag

er

Pool Mgr

Config

<ServicePool>

<ServiceName>ResizeImage</ServiceName>

<VMImage>ami-39ba5df0</VMImage>

<WorkQueue>ResizeImage-input</WorkQueue>

<RampUpInterval>1</RampUpInterval>

<RampUpDelay>360</RampUpDelay>

<RampDownInterval>1</RampDownInterval>

<RampDownDelay>480</RampDownDelay>

<MinSize>0</MinSize>

<MaxSize>20</MaxSize>

<QueueSizeFactor>20000</QueueSizeFactor>

</ServicePool>

This configuration defines the SLA for this service

That’s all there is to implement

The framework does all the rest

Service Pool


Request SQS Queue

Response SQS Queue

S3 Storage

EC

2

Insta

nce

s

Ingestor

ListenerPool Mgr

Config

Pool of service instances dynamically scales with load

Service Pool Pool of service instances dynamically scales with

load


Service Pool Multiple service pools scale independently


etc.

36

Service Pool Workloads can follow different workflows

Specify the Ingestor’s XML accordingly



Java


Q&A


Java


Shlomo [email protected]

Thank you!

java tech day 2009 - developing cloud computing applications with java

Technology

advantages of cloud

scaling cloud

computing resources

style of computing

utility computing

cloud easy stuff

cloud iaas platforms

javaby shlomo swidler