A Comparative Study of Meta-Heuristic-Based Task Scheduling in Cloud Computing

Abstract

AbstractThe massive advances in web, mobile and computer technologies and its users are exponentially growing. Now the era has come where every user is very much conscious about the word “cloud computing”. The users are rolling in cloud services (storage space, computational power and standalone applications). Hence, the cloud service providers (CSP) are concerned about the quality of service (QoS) to their clients. To make it into action, task scheduling was introduced. The principle goal of task scheduling is to carry out successfully the objectives of both server and its clients. As the traditional task scheduling is not enough to attain the best performance. So, meta-heuristic techniques are required, which can produce a solution close to optimal. This optimal solution decides the mapping of tasks on resources and comes up with results that match the desirable objectives. This paper presented a comparative investigation of meta-heuristic centric task scheduling algorithms, such as ant colony optimization (ACO), particle swarm optimization (PSO), gray wolf optimization (GWO), whale optimization algorithm (WOA) and flower pollination algorithm (FPA) which are being used by many researchers for developing new techniques from last decade.KeywordsTask schedulingAnt colony optimization (ACO)Particle swarm optimization (PSO)Gray wolf optimization (GWO)Whale optimization algorithm (WOA)Flower pollination algorithm (FPA)