An efficient symbiotic organisms search algorithm with chaotic optimization strategy for multi-objective task scheduling problems in cloud computing environment

Abstract

n Cloud Computing model, users are charged according to the usage ofresources and desired Quality of Service (QoS). Multi-objective task schedul-ing problem based on desired QoS is an NP-Complete problem. Due to theNP-Complete nature of task scheduling problems and huge search space pre-sented by large scale problem instances, many of the existing solution algo-rithms cannot effectively obtain global optimum solutions. In this paper, achaotic symbiotic organisms search (CMSOS) algorithm is proposed to solvemulti-objective large scale task scheduling optimization problem on IaaS cloudcomputing environment. Chaotic optimization strategy is employed to generateinitial ecosystem(population), and random sequence based components of thephases of SOS are replaced with chaotic sequence to ensure diversity amongorganisms for global convergence. In addition, chaotic local search strategy isapplied to Pareto Fronts generated by SOS algorithms to avoid entrapment inlocal optima. The performance of the proposed CMSOS algorithm is evaluatedon CloudSim simulator toolkit, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, theperformance of the proposed CMSOS algorithm was found to be competitivewith the existing with the existing multi-objective task scheduling optimiza-tion algorithms. The CMSOS algorithm obtained significant improved optimaltrade-offs between execution time (makespan) and financial cost (cost) with nocomputational overhead. Therefore, the proposed algorithms have potentials toimprove the performance of QoS delivery.