A Presentation on Task Programming

Introduction Computing categories Frameworks Task Based application models Aneka Task-Based programming

◦ Task programming model◦ Task Programming examples

Summary References

OVERVIEW

Task computing:◦ What is Task?

a distinct unit of code, or a program, that can be separated and executed in a remote runtime environment

◦ Difference between Multithreaded Computing and Task computing: Multithreaded programming:

mainly concerned with providing a support for parallelism within a single machine.

Task computing: provides distribution by harnessing the computer power of

several computing nodes

◦ Now clouds have emerged as an attractive solution.

Introduction

A Task computing Scenario

According to the specific nature of the problem, task computing is categorized into:

◦ High-performance computing (HPC)

◦ High-throughput computing (HTC)

◦ Many-task computing (MTC).

Computing Catogories

use of distributed computing facilities for solving problems that need large computing power.

Historically, supercomputers and clusters

are specifically designed to support HPC applications.

HPC

use of distributed computing facilities for applications requiring large computing power over a long period of time.

HTC systems need to be robust and to reliably operate over a long time scale.

Traditionally, computing grids composed of heterogeneous resources (clusters, workstations, and volunteer desktop machines) have been used to support HTC.

HTC

aims to bridge the gap between HPC and HTC.

MTC is similar to HTC, but it concentrates on the use of many computing resources over a short period of time to accomplish many computational tasks.

In brief, MTC denotes high-performance computations comprising multiple distinct activities coupled via file system operations.

MTC

Some popular software systems are ◦ Condor◦ Globus Toolkit ◦ Sun Grid Engine (SGE)◦ BOINC ◦ Nimrod/G ◦ Aneka.

Frameworks for task computing

Condor ◦ most widely used and long-lived middleware for

managing clusters, idle workstations, and a collection of clusters.

Sun Grid Engine (SGE)◦ Tool for managing heterogeneous resources and

constitutes middleware for grid computing.

Frameworks (contd……)

The Globus Toolkit ◦ collection of technologies that enable grid

computing.◦ provides a comprehensive set of tools for sharing

computing power, databases, and other services. Nimrod/G

◦ tool for automated modeling and execution of parameter sweep applications over global computational grids.

Contd…….

Berkeley Open Infrastructure for Network Computing (BOINC) ◦ framework for volunteer and grid computing. ◦ allows us to turn desktop machines into volunteer

computing nodes that are leveraged to run jobs when such machines become inactive.

Contd…………

Embarrassingly parallel applications Parameter sweep applications MPI applications Workflow applications with task

dependencies

Task-based application models

Constitute a collection of tasks that are independent from each other and that can be executed in any order.

The tasks might be of the same type or of different types, and they do not need to communicate among themselves.

Supporting frameworks are Globus Toolkit, BOINC, and Aneka.

Embarrassingly parallel applications

Parameter sweep applications are a specific class of embarrassingly parallel applications for which the tasks are identical in their nature and differ only by the specific parameters used to execute them.

Any distributed computing framework that provides support for embarrassingly parallel applica- tions can also support the execution of parameter sweep applications, since the tasks composing the application can be executed independently of each other.

Parameter sweep applications

Message Passing Interface (MPI) is a specification for developing parallel programs that communi- cate by exchanging messages. Compared to earlier models, MPI introduces the constraint of com- munication that involves MPI tasks that need to run at the same time.

MPI applications

Task Programming Model

Figure:- Task Programming Model Scenario

Developing Application with Task Model

Steps:

1. Define Classes With Itask Interface

2. Create a properly Configured AnekaApplication instance

3. Create ITask instances and wrap them into AnekaTask instances

4. Execute the application and wait for its completion

ITask Interface Namespace Aneka.Tasks

{

//Codes public interface ITask {

//Codes public void Execute(); }

}

ITask Interface Implementaion Using System;

Using Aneka.Tasks;

namespace GaussSample

{

[Serializable] public class GaussTask:ITask

{

private double x;

public double X{ get {return this.x; } set {this.x=value;} }

private double y;

public double Y{ get {return this.y; } set {this.y=value;} }

public void Execute()

{

this.y=Math.Exp(-this.x*this.x);

}

}

}

Wrapping an ITask into an Aneka Task Interface

GaussTask gauss=new GaussTask();

AnekaTask task=new AnekaTask(gauss);

task.AddFile("input.txt", FileDataType.Input, FileAttributes.Local);

task.AddFile("result.txt", FileDataType.Output, FileAttributes.Local);

Controlling Task Execution

Computation Logic : Task Classes, AnekaTask

Coordination logic : AnekaApplication Class

AnekaApplication Class Provides following operations:

1. Static and Dynamic task submission

2. Application state and task state monitoring

3. Event based notification of tasks completion or failure.

Task Submission Static Submission

◦ Creation of all tasks to be executed in one one loop and submission as single bag

Dynamic Submission◦ Submission as a result of event-based notification mechanism

Static Submission Configuration conf=new Configuration.GetConfiguration("conf.xml");

conf.SingleSubmission=true;

AnekaApplication<AnekaTask, TaskManager> app=new AnekaApplicatoin<Task, TaskManager>(conf); for(int i=0;i<400;i++)

{

GaussTask gauss=new GaussTask();

gauss.X=i;

AnekaTask task=new AnekaTask(gauss);

app.AddWorkUnit(task);

}

app.SubmitExecute();

Dynamic Submission Events:

WorkUnitFailed

WorkUnitCompleted

File ManagementFiles consists of

◦ Input Data◦ Result of Computation◦ Executable codes or library dependencies

WorkUnit and ApplicationBase Classes for file management

Using FileData Class, the user specifies the file dependencies of tasks and application.

Task Libraries Ready to use tasks

1. File Copy

2. Substitute Operation

3. File Deletion

4. Timed Delay

7. Task Composition

8. Legacy Application Execution

Web Services Integration

Integration with other technologies and applications by means of web services.

Operations supported through web service:

1. Local file copy on the remote node

2. File deletion

3. Parameter substitution

3. Legacy Application execution through the common shell services

ANEKA A PLATFORM FOR ENTERPRISE GRID/CLOUD COMPUTING 29

Anatomy of a Cloud

IAASData Centers

ClustersStorage

Other Grids/Clouds

Virtualization

VM Management & Deployment

Amazon S3, EC2

OpenNebula, Eucalyptus

Mosso

SAASWeb 2.0 Interface

Programming API

Scripting & ProgrammingLanguages

PAASGoogle AppEngine

Microsoft Azure

Manjrasoft Aneka

Google Apps (Gmail, Docs,…)

Salesforce.com

QoS

Met

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SLA

Neg

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Con

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Acco

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Public Cloud

Private Cloud

Socia

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Scie

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Com

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Amus

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CDNs

Fina

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ANEKA A PLATFORM FOR ENTERPRISE GRID/CLOUD COMPUTING 30

Aneka & Clouds

Infrastructure as a Service

Platform as a Service

Software as a Service

Aneka fits into the cloud architecture at the platform layer. This means that it provides a programming based interface for developing distributed application and a virtual execution environment in which

the applications developed according to the published APIs can run.

Public CloudPrivate Cloud

Aneka

Aneka Architecture

ANEKA A PLATFORM FOR ENTERPRISE GRID/CLOUD COMPUTING 31

System Overview

Executor

Scheduler

Executor

Executor Executor

Manager

work units

internet

internet

Aneka enterprise Cloud

Manager

work units

Manager(s)

Client Applications

Workers

Aneka Container

Further on Aneka Aneka supports development of parameter sweep applications.

Aneka can be used to manage workflows through plug ins.

Summary Task based programming is most intuitive approach for distributing the computation of an application over set of nodes

Task Model Comprises set of services(directory, scheduling, execution and storage)

References 1. Buyya, Selvi, Vecchiola. Mastering Cloud Computing. 1st. New Delhi: Mc Graw Hill, 2013.