Potential-driven load distribution for distributed data stream processing

Weihan Wang, Mohamed A. Sharaf, Shimin Guo, M. Tamer Özsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

A large class of applications require real-time processing of continuous stream data resulting in the development of data stream management systems (DSMS). Since many of these applications are distributed, distributed DSMSs are starting to receive attention. In this paper, we focus on an important issue in distributed DSMS operation, namely load distribution to minimize end-to-end latency. We identify the often conflicting requirements of load distribution, and propose a "potential-driven" load distribution approach to mimic the movements of objects in the physical world. Our approach also takes into account heterogeneous machines, different network conditions, and resource constraints. We present experimental results that investigate our algorithms from various aspects, and show that they outperform existing techniques in terms of end-to-end latency.

Original languageEnglish
Title of host publicationACM International Conference Proceeding Series - Proceedings of the 2nd International Workshop on Scalable Stream Processing System 2008, SSPS'08
Pages13-22
Number of pages10
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2nd International Workshop on Scalable Stream Processing System 2008, SSPS'08 - Nantes, France
Duration: Mar 9 2008Mar 9 2008

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2nd International Workshop on Scalable Stream Processing System 2008, SSPS'08
Country/TerritoryFrance
CityNantes
Period3/9/083/9/08

Keywords

  • Data streams
  • Distributed systems
  • Load balancing

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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