Task Assignment in Distributed Systems based on PSO Approach

TL;DR

This paper introduces a hybrid genetic algorithm incorporating Particle Swarm Optimization (PSO) for task assignment in distributed systems, aiming to improve efficiency by reducing scheduling time and enhancing CPU utilization.

Contribution

It presents a novel hybrid GA-PSO approach that outperforms traditional genetic algorithms in task scheduling efficiency and effectiveness.

Findings

Improved CPU utilization compared to GA-based methods

Reduced Makespan in task scheduling

Faster convergence in optimization process

Abstract

In a distributed system, Task Assignment Problem (TAP) is a key factor for obtaining efficiency. TAP illustrates the appropriate allocation of tasks to the processor of each computer. In this problem, the proposed methods up to now try to minimize Makespan and maximizing CPU utilization. Since this problem is NP-complete, many genetic algorithms have been proposed to search optimal solutions from the entire solution space. Disregarding the techniques which can reduce the complexity of optimization, the existing approaches scan the entire solution space. On the other hand, this approach is time-consuming in scheduling which is considered a shortcoming. Therefore, in this paper, a hybrid genetic algorithm has been proposed to overcome this shortcoming. Particle Swarm Optimization (PSO) has been applied as local search in the proposed genetic algorithm in this paper. The results obtained from simulation can prove that, in terms of CPU utilization and Makespan, the proposed approach outperforms the GA-based approach.