An Evolutionary Algorithm Based Performance Analysis of Multiprocessor Computers through Energy and Schedule Length Model

Abstract

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 parallel tasks onto the processors is known to be NP- Hard problem. With growing of applications of the embedded system technology, energy efficiency and timing requirement are becoming important issues for designing real time embedded systems. This paper focuses the combinational optimization problem, namely, the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint for independent parallel tasks on multiprocessor computers. 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 analytically and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).