A New Method Based on Memetic Algorithm for Task Scheduling in Distributed Systems

Tasks scheduling problem is a key factor for a distributed system in order to achieve better efficiency. The problem of tasks scheduling in a distributed system can be stated as allocating tasks to processor of each computer. The objective of this problem is minimizing makespan and communication cost while maximizing CPU utilization. Scheduling problem is known as NP-complete. Hence, many genetic algorithms have been proposed to search optimal solutions from entire solution space. However, these existing approaches are going to scan the entire solution space without consideration to techniques that can reduce the complexity of the optimization. In other words, the main shortcoming of these approaches is to spend much time doing scheduling and hence need to exhaustive time. Therefore, in this paper we use memetic algorithm to cope with this shortcoming. We apply Learning automata, simulated annealing, and Tabu search as local search in our proposed memetic algorithm. Extended simulation results demonstrate that the proposed methods outperform the existent GA-based method in term of communication cost, CPU utilization and makespan. Keywords-Task scheduling, Memetic algorithm, Simulated annealing