Parallel Extremal Optimization with Guided Search and Crossover Applied to Load Balancing

  • Eryk Laskowski
  • Marek Tudruj
  • Ivanoe De Falco
  • Umberto Scafuri
  • Ernesto Tarantino
  • Richard Olejnik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9573)


Extremal Optimization is a nature-inspired optimization method which features small computational and memory complexity. Due to these features it can be efficiently used as an engine for processor load balancing. The paper presents how improved Extremal Optimization algorithms can be applied to processor load balancing. Extremal Optimization detects the best strategy of tasks migration leading to balanced application execution and reduction in execution time. The proposed algorithm improvements cover several aspects. One is algorithms parallelization in a multithreaded environment. The second one is adding some problem knowledge to improve the convergence of the algorithms. The third aspect is the enrichment of the parallel algorithms by inclusion of some elements of genetic algorithms – namely the crossover operation. The load balancing based on improved Extremal Optimization aim at better convergence of the algorithm, smaller number of task migrations to be done and reduced execution time of applications. The quality of the proposed algorithms is assessed by experiments with simulated parallelized load balancing of distributed program graphs.


Nature inspired optimization Load balancing Extremal optimization Distributed computing 


  1. 1.
    Barker, K., Chrisochoides, N.: An evaluation of a framework for the dynamic load balancing of highly adaptive and irregular parallel applications. In: Proceedings of the ACM/IEEE Conference on Supercomputing. ACM Press, Phoenix (2003)Google Scholar
  2. 2.
    Boettcher, S., Percus, A.G.: Extremal optimization: methods derived from coevolution. In: Proceedings of the Genetic and Evolutionary Computation Conference(GECCO 1999), pp. 825–832. Morgan Kaufmann, San Francisco (1999)Google Scholar
  3. 3.
    De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M.: Load balancing in distributed applications based on extremal optimization. In: Esparcia-Alcázar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 52–61. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M.: Improving extremal optimization in loadbalancing by local search. In: Esparcia-Alcázar, A.I., Mora, A.M. (eds.) EvoApplications 2014. LNCS, vol. 8602, pp. 51–62. Springer, Heidelberg (2014)Google Scholar
  5. 5.
    De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M.: Extremal optimization applied to load balancing in execution of distributed programs. Appl. Soft Comput. 30(5), 501–513 (2015)CrossRefGoogle Scholar
  6. 6.
    Khan, R.Z., Ali, J.: Classification of task partitioning and load balancing strategies in distributed parallel computing systems. Int. J. Comput. Appl. 60(17), 48–53 (2012)Google Scholar
  7. 7.
    Mishra, M., Agarwal, S., Mishra, P., Singh, S.: Comparative analysis of various evolutionary techniques of load balancing: a review. Int. J. Comput. Appl. 63(15), 8–13 (2013)Google Scholar
  8. 8.
    Randall, M., Lewis, A.: An extended extremal optimisation model for parallel architectures. In: 2nd IEEE International Conference on e-Science and Grid Computing, e-Science 2006, p. 114 (2006)Google Scholar
  9. 9.
    Sneppen, K., et al.: Evolution as a self-organized critical phenomenon. Proc. Natl. Acad. Sci. 92, 5209–5213 (1995)CrossRefGoogle Scholar
  10. 10.
    Tamura, K., Kitakami, H., Nakada, A.: Reducing crossovers in reconciliation graphs with extremal optimization (in japanese). Trans. Inf. Process. Soc. Jpn. 49(4) (TOM 20), 105–116 (2008)Google Scholar
  11. 11.
    Tamura, K., Kitakami, H., Nakada, A.: Island-model-based distributed modified extremal optimization for reducing crossovers in reconciliation graph. Transactions on Engineering Technologies. LNCS, vol. 275. Springer, New York (2013)Google Scholar
  12. 12.
    Tamura, K., Kitakami, H., Nakada, A.: Distributed modified extremal optimization using island model for reducing crossovers in reconciliation graph. Eng. Lett. 21(2), EL\(\_\)21\(\_\)2\(\_\)05, 81–88 (2013)Google Scholar
  13. 13.
    Zeigler, B.: Hierarchical, modular discrete-event modelling in an object-oriented environment. Simulation 49(5), 219–230 (1987)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

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

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