Abstract
The paper presents a multi-objective load balancing algorithm based on Extremal Optimization in execution of distributed programs. The Extremal Optimization aims in defining task migration as a means for improving balance in loading executive processors with program tasks. In the proposed multi-objective approach three objectives relevant in processor load balancing for distributed applications are jointly optimized. These objectives include: balance in computational load of distributed processors, total volume of inter-processor communication between tasks and task migration metrics. In the proposed Extremal Optimization algorithms a special approach called Guided Search is applied in selection of a new partial solution to be improved. It is supported by some knowledge of the problem in terms of computational and communication loads influenced by task migration. The proposed algorithms are assessed by simulation experiments with distributed execution of program macro data flow graphs.
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De Falco, I., Laskowski, E., Olejnik, R., Scafuri, U., Tarantino, E., Tudruj, M. (2018). Multi-Objective Extremal Optimization in Processor Load Balancing for Distributed Programs. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2017. Lecture Notes in Computer Science(), vol 10778. Springer, Cham. https://doi.org/10.1007/978-3-319-78054-2_17
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