M. H. KASHANI & M. JAHANSHAHI: A NEW METHOD BASED ON MEMETIC IJSSST, Vol. 10, No. 5, May 2009 26 ISSN: 1473-804x online, 1473-8031 print A New Method Based on Memetic Algorithm for Task Scheduling in Distributed Systems M. H. Kashani M. Jahanshahi Computer Engineering Department, Islamic Azad University, Shahr-e-Qods branch Tehran, Iran Computer Engineering Department, Islamic Azad University, Central Tehran branch Tehran, Iran kashani@shahryariau.ac.ir mjahanshahi@iauctb.ac.ir Abstract-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 I. INTRODUCTION With many achievements such as technology, computer architectures, and software packages, distributed systems are used in a great variety of applications. The problem of task scheduling in these systems has received a large amount of attention recently. Task scheduling in a distributed system can be stated as allocating tasks to processors of each computer such that the optimum performance is obtained. The aim of task scheduling is minimizing makespan (job completion time) and communication cost while maximizing CPU utilization. This problem is known as NP-complete [24]. There are two categories for task scheduling; static and dynamic. In dynamic scheduling, schedules create during run time and no knowledge of task is in hand until it arrives. While in static scheduling, schedules are created before run time and cannot change. Similarly, tasks must all be known in advance. In other words a static task scheduling algorithm schedules a set of tasks with known processing and communication characteristics on processors to optimize some performance metric, such as makespan, communication cost and CPU utilization. In this paper we focus on static scheduling. Several methods have been proposed to solve scheduling problem. The proposed methods can be generally classified into three categories: Graph-theory- based approaches [25], mathematical models-based methods [26] and heuristic Techniques [27-30]. As mentioned above the scheduling problem has been known to be NP-complete. Therefore using heuristic Techniques can solve this problem more efficiently. Three most well-known heuristics are the iterative improvement algorithms [35], the probabilistic optimization algorithms, and the constructive heuristics. In the probabilistic optimization group, GA-based methods [31-37] are considerable. The main distinction among them is chromosomal representation used for a schedule. However, these approaches scan the entire solution space without consideration to techniques that can reduce the complexity of the optimization. In other words their main shortcoming is to spend much time doing scheduling. This shortcoming of GA-based methods can be reduced by combing GA with another optimization technique. Hence this paper proposes a new algorithm by using memetic algorithm to cope with this shortcoming. We apply Learning automata with updating algorithm P R L  and I R L  , Simulated annealing and Tabu search as local search in memetic. Simulation results demonstrate that our proposed methods outperform the existent GA-based method in term of communication cost, CPU utilization and Make span. The rest of this paper is organized as follows: In section 2 used methods as local search in the proposed memetic algorithm are presented. Proposed methods come in section 3. Simulation results are given in section 4. Section 5 concludes the paper.