Abstract
The paper proposes and discusses distributed processor load balancing algorithms which are based on nature inspired approach of multi-objective Extremal Optimization. Extremal Optimization is used for defining task migration aiming at processor load balancing in execution of graph-represented distributed programs. The analysed multi-objective algorithms are based on three or four criteria selected from the following four choices: the balance of computational loads of processors in the system, the minimal total volume of application data transfers between processors, the number of task migrations during program execution and the influence of task migrations on computational load imbalance and the communication volume. The quality of the resulting load balancing is assessed by simulation of the execution of the distributed program macro data flow graphs, including all steps of the load balancing algorithm. It is done following the event-driven model in a simulator of a message passing multiprocessor system. The experimental comparison of the multi-objective load balancing to the single objective algorithms demonstrated the superiority of the multi-objective approach.
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References
Boettcher, S., Percus, A.G.: Extremal optimization: methods derived from co-evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), pp. 825–832. Morgan Kaufmann, San Francisco (1999)
Lu, Y.Z., Chen, Y.W., Chen, M.R., Chen, P., Zeng, G.Q.: Extremal Optimization: Fundamentals, Algorithms, and Applications, p. 334. CRC Press, Boca Raton (2016)
De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M.: Improving extremal optimization in load balancing by local search. In: Esparcia-Alcázar, A.I., Mora, A.M. (eds.) EvoApplications 2014. LNCS, vol. 8602, pp. 51–62. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45523-4_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, 501–513 (2015)
De Falco, I., Scafuri, U., Laskowski, E., Tarantino, E., Olejnik, R., Tudruj, M.: Effective processor load balancing using multi-objective parallel extremal optimization. In: GECCO 2018, Companion Material Proceedings, pp. 1292–1299. ACM (2018)
Xu, C., Lau, F.C.M.: Load Balancing in Parallel Computers: Theory and Practice. Kluwer Academic Publishers, Dordrecht (1997)
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)
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)
Tanvi, Kaur, K.: A study on extremal optimization based load balancing techniques. Indian J. Comput. Sci. Eng. 8(2), 95–101 (2017)
Ahmed, E., Elettreby, M.F.: On multi-objective evolution model. Int. J. Mod. Phys. C 15(9), 1189–1195 (2004)
Gómez-Meneses, P., Randall, M., Lewis, A.: A hybrid multi-objective extremal optimisation approach for multi-objective combinatorial optimisation problems. Bond University, Griffith University, Australia (2010)
Zeigler, B.: Hierarchical, modular discrete-event modelling in an object-oriented environment. Simulation 49(5), 219–230 (1987)
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De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M. (2019). Distributed Processor Load Balancing Based on Multi-objective Extremal Optimization. In: Montella, R., Ciaramella, A., Fortino, G., Guerrieri, A., Liotta, A. (eds) Internet and Distributed Computing Systems . IDCS 2019. Lecture Notes in Computer Science(), vol 11874. Springer, Cham. https://doi.org/10.1007/978-3-030-34914-1_16
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