Improved symbiotic organism search based approach for scheduling jobs in cloud

Abstract

Optimal task scheduling plays an important role in improving the performance of cloud computing. As it has been classified as a NP-complete problem, researchers are not able to get an exact solution for this issue. Symbiotic Organism Search (SOS) algorithm is the latest meta-heuristic technique that is widely used for finding the solution of optimization problems. Improved SOS (ISOS) algorithm is imitated from symbiotic relationships that exist among different organisms of an ecosystem. This work presents a scheduling algorithm based on ISOS Algorithm for the best possible mapping of various tasks on available cloud resources. The proposed algorithm is aimed to minimize two-objective functions that aremakespan and cost. To validate the performance of presented work, it is compared with PSO algorithm. Simulation results show that ISOS algorithm gives 19.71–49.50% improvement in terms of makespan and 27.65–42.73% improvement in terms of cost over PSO algorithm when the number of tasks is varied from 100 to 500.