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.
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References
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)
Olejnik, R., De Falco, I., Laskowski, E., 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)
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, Phoenix. ACM Press (2003)
Willebeek-LeMair, M.H., Reeves, A.P.: Strategies for dynamic load balancing on highly parallel computers. IEEE Trans. on Parallel and Distributed Systems 4, 979–993 (1993)
Xu, C., Francis, C., Lau, M.: Load balancing in parallel computers: Theory and Practice. Kluwer Academic Publishers, Norwell (1997)
Khan, R.Z., Ali, J.: Classification of task partitioning and load balancing strategies in distributed parallel computing systems. International Journal of Computer Applications 60(17), 48–53 (2012)
Munetomo, M., Takai, M.N.K., Sato, Y.: A stochastic genetic algorithm for dynamic load balancing in distributed systems. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3795–3799. IEEE Press (1995)
Zomaya, A.Y., Teh, Y.-H.: Observations on using genetic algorithms for dynamic load-balancing. IEEE Trans. on Parallel and Distributed Systems 12(9), 899–911 (2001)
Uyar, A.S., Harmanci, A.E.: Application of an improved diploid genetic algorithm for optimizing performance through dynamic load balancing. In: Proceedings of 2002 WSEAS International Conferences. WSEAS Press (2002)
Lin, C.-C., Deng, D.-J.: Dynamic load balancing in cloud-based multimedia system using genetic algorithm. Chang, R.-S., et al (eds.) Advances in Intelligent Systems & Applications, SIST 20, pp. 461–470. Springer, Heidelberg (2013)
Mishra, M., Agarwal, S., Mishra, P., Singh, S.: Comparative analysis of various evolutionary techniques of load balancing: a review. International Journal of Computer Applications 63(15) (2013)
Sneppen, K., et al.: Evolution as a self-organized critical phenomenon. Proc. Natl. Acad. Sci. 92, 5209–5213 (1995)
Karypis, G., Kumar, V.: Multilevel graph partitioning schemes. In: Proc. 24th Intern. Conf. Par. Proc., III. pp. 113–122. CRC Press (1995)
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De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M. (2014). Improving Extremal Optimization in Load Balancing by Local Search. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_5
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DOI: https://doi.org/10.1007/978-3-662-45523-4_5
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