A PSO-based Genetic Algorithm for Scheduling of Tasks in a Heterogeneous Distributed System

Abstract

The static task scheduling problem in distributed systems is important because of optimal usage of available machines and accepted computation time for scheduling algorithm. A PSO-based hybrid algorithm is presented to schedule the tasks represented by a Directed Acyclic Graph (DAG) to a bounded number of heterogeneous processors such that its schedule length is optimized. The algorithm first generate feasible initial solutions by using some effective list scheduling strategy and then evolve the solution by using crossover and mutation operator. These operators are modified to ensure the validity of the solutions. And a PSO strategy is also used to improve the quality of the solutions by using local and global information together. The performance of the algorithm is demonstrated by comparing the scheduling length ratio with the existing effectively scheduling algorithms, Heterogeneous Earliest Finish Time and Genetic Scheduling algorithm.