LOW COMPLEXITY CONSTRAINTS FOR ENERGY AND PERFORMANCE MANAGEMENT OF HETEROGENEOUS MULTICORE PROCESSORS USING DYNAMIC OPTIMIZATION

Optimization in multicore processor environment is significant in real world dynamic applications, as it is crucial to find and track the change effectively over time, which requires an optimization algorithm. In massively parallel processing multicore processor architectures, like other population based metaheuristics Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling can be effectively implemented. In this study we discuss possible approaches to parallelize CBFPSO in multicore system, which uses different constraints; to exploit parallelism are explored and evaluated. Due to the ability of keeping good balance between convergence and maintenance, for real world applications, among the various algorithms for parallel architecture optimization CBFPSOs are attracting more and more attentions in recent years. To tackle the challenges of parallel architecture optimization, several strategies have been proposed, to enhance the performance of Particle Swarm Optimization (PSO) and have obtained success on various multicore parallel architecture optimization problems. But there still exist some issues in multicore architectures which require to be analyzed carefully. In this study, a new Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling for multicore architecture is proposed, which updates the velocity and position by two bacterial behaviours, i.e., reproduction and elimination dispersal. The performance of CBFPSO is compared with the simulation results of GA and the result shows that the proposed algorithm has pretty good performance on almost all types of cores compared to GA with respect to completion time and energy consumption.