Publication Type:

Journal Article


Soft Computing, Springer Verlag, p.1-15 (2017)



Computational complexity, Energy optimization, Energy utilization, Evolutionary algorithms, Fault tolerance, Genetic algorithms, Multi processor systems, Multiobjective optimization, Multiprocessing systems, Optimization, Schedule length, Task graph, Task-scheduling, Wheels


In a multiprocessor system, scheduling is an NP-hard problem, and solving it using conventional techniques demands the support of evolutionary algorithms such as genetic algorithms (GAs). Handling the energy consumption issues, while delivering the desired performance for a system, is also a challenging task. In order to achieve these goals, this paper proposes a GA-based method for optimizing the energy consumption and performance of multiprocessor systems using a weighted-sum approach. A performance optimization algorithm with two different selection operators, namely the proportional roulette wheel selection (PRWS) and the rank-based roulette wheel selection (RRWS), is proposed, and the impact of adding elitism in the GA is investigated. Simulation results show that for a specific task graph, using the considered selection operators with elitism yields, respectively, 16.80, 17.11 and 17.82% reduction in energy consumption with a deviation in finish time of 2.08, 2.01 and 1.76 ms when an equal weight factor of 0.5 is considered. This confirms that the selection operator RRWS is superior to PRWS. It is also seen that using elitism enhances the optimization procedure. For a given specific workload, the average percentage reduction in energy consumption with varying weight vector is in the range 12.57–19.51%, with a deviation in finish time of the schedule varying between 1.01 and 2.77 ms. © 2017 Springer-Verlag GmbH Germany


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Cite this Research Publication

A. S. Pillai, Singh, K., Saravanan, V., Anpalagan, A., Woungang, I., and Barolli, L., “A genetic algorithm-based method for optimizing the energy consumption and performance of multiprocessor systems”, Soft Computing, pp. 1-15, 2017.