Symbiotic Organism Search Optimization Based Task Scheduling in Cloud Computing Environment

Abstract

Efficient task scheduling is one of the major steps for effectively harnessing the potential of cloud computing. In cloud computing, a number of tasks may need to be scheduled on different virtual machines in order to minimize makespan and increase system utilization. Task scheduling problem is NP-complete, hence finding an exact solution is intractable especially for large task sizes. This paper presents a Discrete Symbiotic Organism Search (DSOS) algorithm for optimal scheduling of tasks on cloud resources. Symbiotic Organism Search (SOS) is a newly developed metaheuristic optimization technique for solving numerical optimization problems. SOS mimics the symbiotic relationships (mutualism, commensalism, and parasitism) exhibited by organisms in an ecosystem. Simulation results revealed that DSOS outperforms Particle Swarm Optimization (PSO) which is one of the most popular heuristic optimization techniques used for task scheduling problems. DSOS converges faster when the search gets larger which makes it suitable for large-scale scheduling problems. Analysis of the proposed method conducted using -test showed that DSOS performance is significantly better than that of PSO particularly for large search space.