Research Article

Management of real-time streaming data Grid services

  1. Geoffrey Fox,
  2. Galip Aydin,
  3. Hasan Bulut,
  4. Harshawardhan Gadgil,
  5. Shrideep Pallickara,
  6. Marlon Pierce*,
  7. Wenjun Wu

Article first published online: 28 SEP 2006

DOI: 10.1002/cpe.1090

Issue

Concurrency and Computation: Practice and Experience

Concurrency and Computation: Practice and Experience

Special Issue: Autonomous Grid Computing

Volume 19, Issue 7, pages 983–998, May 2007

Additional Information

How to Cite

Fox, G., Aydin, G., Bulut, H., Gadgil, H., Pallickara, S., Pierce, M. and Wu, W. (2007), Management of real-time streaming data Grid services. Concurrency Computat.: Pract. Exper., 19: 983–998. doi: 10.1002/cpe.1090

Author Information

  1. Community Grids Laboratory, Indiana University, 501 North Morton Street, Suite 224, Bloomington, IN 47404, U.S.A.

*Community Grids Laboratory, Indiana University, 501 North Morton Street, Suite 224, Bloomington, IN 47404, U.S.A.

Publication History

  1. Issue published online: 26 MAR 2007
  2. Article first published online: 28 SEP 2006
  3. Manuscript Accepted: 4 MAY 2006
  4. Manuscript Received: 13 MAR 2006

Funded by

  • U.S. Department of Energy (SciDAC Program). Grant Number: DE-FC02-02ER2615
  • NASA (Advanced Information Systems Technology Program). Grant Number: 1253656

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Keywords:

  • real-time systems;
  • Web services;
  • Grid computing

Abstract

We discuss our message-based approach to managing real-time data streams and building higher level services to produce and consume them. Our messaging system acts as a substrate that can be used to provide qualities of service to various streaming applications ranging from audio–video collaboration systems to sensor Grids. The messaging substrates are composed of distributed, hierarchically arranged message broker networks. Services such as filters are deployed along the edges of the network. We discuss the role of management systems for both broker networks and filter services: broker network topologies must be created and maintained, and distributed filters must be arranged in appropriate sequences. These managed broker networks may be applied to a wide range of problems. We discuss applications to audio–video collaboration in some detail and also describe applications to streaming Global Positioning System data streams. These provide specific application filters that can transform and republish message streams to the broker system. Copyright © 2006 John Wiley & Sons, Ltd.

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