Hybridization of Genetic and Group Search Optimization Algorithm for Deadline-Constrained Task Scheduling Approach

Abstract

Abstract Cloud computing is an emerging technology in distributed computing, which facilitates pay per model as per user demand and requirement. Cloud consists of a collection of virtual machines (VMs), which includes both computational and storage facility. In this paper, a task scheduling scheme on diverse computing systems using a hybridization of genetic and group search optimization (GGSO) algorithm is proposed. The basic idea of our approach is to exploit the advantages of both genetic algorithm (GA) and group search optimization algorithms (GSO) while avoiding their drawbacks. In GGSO, each dimension of a solution symbolizes a task, and a solution, as a whole, signifies all task priorities. The important issue is how to assign user tasks to maximize the income of infrastructure as a service (Iaas) provider while promising quality of service (QoS). The generated solution is competent to assure user-level (QoS) and improve Iaas providers’ credibility and economic benefit. The GGSO method also designs the producer, scrounger ranger, crossover operator, and suitable fitness function of the corresponding task. According to the evolved results, it has been found that our algorithm always outperforms the traditional algorithms.