Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm

Abstract

Cloud computing is the delivery of computing services over the internet. Cloud services allow individuals and other businesses organization to use data that are managed by third parties or another person at remote locations. Most Cloud providers support services under constraints of Service Level Agreement (SLA) definitions. The SLAs are composed of different quality of service (QoS) rules promised by the provider. A cloud environment can be classified into two types: computing clouds and data clouds. In computing cloud, task scheduling plays a vital role in maintaining the quality of service and SLA. Efficient task scheduling is one of the major steps for effectively harnessing the potential of cloud computing. This paper explores the task scheduling algorithm using a hybrid approach, which combines desirable characteristics of two of the most widely used biologically-inspired heuristic algorithms, the genetic algorithms (GAs) and the bacterial foraging (BF) algorithms in the computing cloud. The main contributions of this article are twofold. First, the scheduling algorithm minimizes the makespan and second; it reduces the energy consumption, both economic and ecological perspectives. Experimental results show that the performance of the proposed algorithm outperforms than those of other algorithms regarding convergence, stability, and solution diversity. Keywords: Cloud computing, Resource allocation, Pareto solutions, Bacteria foraging, Genetic algorithms