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A Space-Based Generic Pattern for Self-Initiative Load Clustering Agents

  • Eva Kühn
  • Alexander Marek
  • Thomas Scheller
  • Vesna Sesum-Cavic
  • Michael Vögler
  • Stefan Craß
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7274)

Abstract

Load clustering is an important problem in distributed systems, which proper solution can lead to a significant performance improvement. It differs from load balancing as it considers a collection of loads, instead of normal data items, where a single load can be described as a task. Current approaches that treat load clustering mainly lack of provisioning a general framework and autonomy. They are neither agent-based nor configurable for many topologies. In this paper we propose a generic framework for self-initiative load clustering agents (SILCA) that is based on autonomous agents and decentralized control. SILCA is a generic architectural pattern for load clustering. The SILCA framework is the corresponding implementation and thus supports exchangeable policies and allows for the plugging of different algorithms for load clustering. It is problem independent, so the best algorithm or combination of algorithms can be found for each specific problem. The pattern has been implemented on two levels: In its basic version different algorithms can be plugged, and in the extended version different algorithms can be combined. The flexibility is proven by means of nine algorithms. Further contributions are the benchmarking of the algorithms, and the working out of their best combinations for different topologies.

Keywords

Agents Load Clustering Load Balancing Coordination Tuple Space 

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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Eva Kühn
    • 1
  • Alexander Marek
    • 1
  • Thomas Scheller
    • 1
  • Vesna Sesum-Cavic
    • 1
  • Michael Vögler
    • 1
  • Stefan Craß
    • 1
  1. 1.Institute of Computer LanguagesVienna University of TechnologyViennaAustria

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