Multi-objective-Oriented Cuckoo Search Optimization-Based Resource Scheduling Algorithm for Clouds

Abstract

Scheduling problems in cloud computing environment are mostly influenced by multi-objective optimization but frequently deal with using single-objective algorithms. The algorithms need to resolve multi-objective problems which are significantly different from the procedure or techniques used for single-objective optimizations. For this purpose, meta-heuristic algorithms always show their strength to deal with multi-objective optimization problems. In this research article, we present an innovative Multi-objective Cuckoo Search Optimization (MOCSO) algorithm for dealing with the resource scheduling problem in cloud computing. The main objective of resource scheduling problem is to reduce the cloud user cost and enhance the performance by minimizing makespan time, which helps to increase the revenue or profit for cloud providers with maximum resource utilization. Therefore, the proposed MOCSO algorithm is a new method for solving multi-objective resource scheduling problems in IaaS cloud computing environment. Moreover, the effects of the proposed algorithm are analyzed and evaluated by comparison with state-of-the-art multi-objective resource scheduling algorithms using simulation framework. Results obtained from simulation show that the proposed MOSCO algorithm performs better than MOACO, MOGA, MOMM and MOPSO, and balance multiple objectives in terms of expected time to completion and expected cost to completion matrices for resource scheduling in IaaS cloud computing environment.