Improving Extremal Optimization in Load Balancing by Local Search

  • Ivanoe De Falco
  • Eryk Laskowski
  • Richard Olejnik
  • Umberto Scafuri
  • Ernesto Tarantino
  • Marek Tudruj
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)

Abstract

The paper concerns the use of Extremal Optimization (EO) technique in dynamic load balancing for optimized execution of distributed programs. EO approach is used to periodically detect the best candidates for task migration leading to balanced execution. To improve the quality of load balancing and decrease time complexity of the algorithms, we have improved EO by a local search of the best computing node to receive migrating tasks. The improved guided EO algorithm assumes a two-step stochastic selection based on two separate fitness functions. The functions are based on specific program models which estimate relations between the programs and the executive hardware. The proposed load balancing algorithm is compared against a standard EO-based algorithm with random placement of migrated tasks and a classic genetic algorithm. The algorithm is assessed by experiments with simulated load balancing of distributed program graphs and analysis of the outcome of the discussed approaches.

Keywords

Distributed program design Extremal optimization Load balancing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ivanoe De Falco
    • 1
  • Eryk Laskowski
    • 2
  • Richard Olejnik
    • 3
  • Umberto Scafuri
    • 1
  • Ernesto Tarantino
    • 1
  • Marek Tudruj
    • 2
    • 4
  1. 1.Institute of High Performance Computing and NetworkingCNRNaplesItaly
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  3. 3.Computer Science LaboratoryUniversity of Science and Technology of LilleVilleneuve-d’AscqFrance
  4. 4.Polish-Japanese Institute of Information TechnologyWarsawPoland

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