Real-World Distributed

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Henri E. Bal Jason Maassen 

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CONTENTS

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INTRODUCTION REALWORLD DISTRIBUTED COMPUTING IBIS ARCHITECTURE IBIS PROGRAMMING SYSTEM IPL SMART SOCKETS IBIS PROGRAMMING MODELS IBIS DEPLOYMENT SYSTEM JAVAGAT ZORILLA IBIS DEPLOY MUTIMEDIA CONTENT ANALYSIS EXPERIMENTAL EVALUATION FUTURE SCOPE CONCLUSION

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For more complex applications existing scheduling system is limited.

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Ibis allows easy programming and development of

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REAL-WORLD DISTRIBUTED COMPUTING

 Ad-hoc collection of compute resources that communicate with one another via some network connection constitutes a real world distributed computing system.

 Writing applications of such system is difficult.

 High performance distributed system can be abstracted these complexities by a single software system that applies to any real world distributed system  These system have two logically independent subsystems such as programming system and deployment system.

DISTRIBUTED SYSTEM

IBIS ARCHITECTURE

 Ibis uses java vertual machine technology.

 Ibis system architecture follows the duel system approach such as the programming system and deployment system.

 Programming system should allow efficient programming models that support fault tolarence and malleability.Implemented in the Ibis portability layer(IPL)  Deployment system contains a GUI and a library for deploying and managing applications implemented on a middleware interoperatability layer(MIL).

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 It should also provide support for distributed file mamagement,user authentication,resource manegement and interoperatability between different middleware system.

 Ibis is modular and flexible.

IBIS PROGRAMMING SYSTEM IPL

 IPL is a java based communication library.

 In IPL the resources can be added or removed dynamically.

 It provides a range of communication primitives including those for point-to point and multicast communication.

 It avoids the copy overhead.

 It incorporates a mechanism ,Join Elect Leave(JEL).

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 JEL is based on the concept of signalling.

 JEL provides the building blocks for fault tolerance.

 Java IPL implementations are available using Ibis SmartSocket library,TCP UDP and bluetooth.

 Also performs implementation using specialised non java libraries such as MX,MPI.

SMART SOCKETS

 Incoming traffic at a node may be restricted by a firewall.  SmartSocket library solves the problem like reverse connection setup,overlay routing,and secure Shell (SSH)tunneling.

 When creating a connection ,smartsocket initially tries to set up a regular TCP connection.

 Smartsockets also handles machine with multiple network address via multihoming.

 Extra identity checks in the protocol ensure that it reaches the correct destination.

IBIS PROGRAMMING MODELS

 MPJ,MPI binding for java;  Satin, a divide and conquer model;  RMI, object oriented remote procedural call;  Group method invocation(GMI)a generalisation of RMI to group communication, including multicast and all to all communication.

 Maestro, a fault tolerant and self optimizing data flow model.

 Jorus, a programming model for data parallel multimedia applications.

IBIS DEPLOYMENT SYSTEM

JavaGAT

 The JavaGAT provides a high-level API that facilitates development of complex applications.

 This API is object oriented.

 The primitives provide access to remote data, user authentication, resource management, and storing of application-specific data  The JavaGAT also uses intelligent dispatching to integrate multiple middleware systems with different and incomplete functionality into a single, consistent system.

1 import org.gridlab.gat.*; 2 import org.gridlab.gat.io.File; 3 4 public class Copy { 5 public static void main(String [] args)throws Exception { 6 GATContext context = newGATContext(); 7 URI source = new URI(args[0]); 8 URI dest = new URI(args[1]); 9 10 File file = GAT.createFile(context,source); // Create a GAT file 11 12 file.copy(dest); // The actual file or directory copy 13 14 GAT.end(); // Shutdown the JavaGAT 15 } 16 }

Zorilla

 Zorilla is a lightweight P2P middleware that runs on any real-world distributed system.

 Zorilla has no central components and is easy to set up and maintain.

 It supports fault tolerance and malleability by implementing all functionality using P2P techniques.

 Zorilla which performed with the help of JavaGAT.

Ibis deploy

 Ibis deploy provides a simple and generic API and GUI that can automatically perform commonly used deployment scenarios.

 When a distributed Ibis application is running, It also automatically uploads the program codes, libraries, and input files and automatically downloads the output files.

 Run multiple distributed applications concurrently.

APPLICATION EXAMPLE: MULTIMEDIA CONTENT ANALYSIS

DIGITAL CAMERA

EXPERIMENTAL EVALUATION

   Distributed ACCI super computer 3.

Five clustered distributed grid systems.

Amazon EC2 cloud system.

RELATED WORKS

 ProActive.

 Phoenix.

 The GRID superscalar framework.

 The GridRPC specification.

 The Open Grid Forum.

APPLICATIONS

 Multimedia computing.

 Spectroscopic data processing.

 Human brain-scan analysis.  Automatic grammar learning.

OPEN PROBLEMS AND FUTURE WORK

 Non-Java applications could also use the IPL through the JNI which is complicated.  For high programming applications it cannot be used.

 Intelligent-dispatching technique leads to more complex error reporting and debugging if operations fail.

 JavaGAT introduces runtime overhead.

 Visual debugging profiling tools should developed for solving these problems

CONCLUSION

 Ibis reduces the effort needed to create and deploy applications for real-world distributed systems.

 To achieve this, it integrates solutions to many fundamental distributed computing problems in a single modular programming and deployment system.  Two-way connectivity is rare in a real-world distributed system. However, Smart Sockets achieves this in a transparent manner.

 Ibis also tries to make distributed programming easier by providing high-level programming models on top of these mechanisms

REFERENCES

 I. Foster, C. Kesselman, and S. Tuecke, “The Anatomy of the Grid: Enabling Scalable Virtual Organizations,” Int’l J. High-Performance Computing Applications, Aug. 2001, pp. 200-222.

 D. Butler, “The Petaflop Challenge,” Nature, 5 July 2007, pp. 6-7.

 K. Verstoep et al., “Experiences with Fine-Grained Distrib- uted Supercomputing on a 10G Testbed,” Proc. 2008 8th IEEE Int’l Symp. Cluster Computing and the Grid (CCGrid 08), IEEE CS Press, 2008, pp. 376-383