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Particle Swarm Optimization based Sequential and Parallel Tasks Scheduling Model for Heterogeneous Multiprocessor Systems

Kumar P. R., Babu P., Palani S.

Fundamenta Informaticae

2015

Vol. 139, nr 1

43--65

Multiprocessors have emerged as a powerful computing means for running real-time applications, especially where a uniprocessor system would not be sufficient enough to execute all the tasks. The high performance and reliability of multiprocessors have made them a powerful computing resource. Such computing environment requires an efficient algorithm to determine when and on which processor a given task should be executed. In multiprocessor systems, an efficient scheduling of sequential and parallel tasks onto the processors is known to be NP- Hard problem. In this paper, the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint on homogeneous and heterogeneous multiprocessor computers through independent sequential and parallel tasks are proposed. These problems emphasize the tradeoff between power and performance and are defined such that the power-performance product is optimized by fixing one factor and minimizing the other and vice versa. The performance of the proposed algorithm with optimal solution is validated using Particle Swarm Optimization (PSO) The PSO algorithm achieves 47.5% and 32% of power savings for scheduling sequential and parallel tasks to the processors respectively and also 45.5% of energy saving are achieved for scheduling both sequential or parallel tasks to the processors.