Particle Swarm Algorithm for Tasks Scheduling in Distributed Heterogeneous System

Abstract

A distributed heterogeneous system consists of a suite of processors or machines with different processing capacities. It can be performance-to-cost efficient to meet the diverse computation requirements if properly deployed. Task scheduling is a crucial issue to improve the efficiency of this architecture. In this paper, we incorporate an efficient population-based search technique, particle swarm optimization (PSO), with list scheduling and propose a hybrid PSO algorithm for tasks scheduling. We also compare a few assigning rules to select target machine with different processing speeds for different tasks. The experiment results show that the proposed algorithm outperforms other algorithms in these aspects of performance and scalability