An adaptive symbiotic organisms search for constrained task scheduling in cloud computing.

Abstract

Metaheuristic algorithms have been effective in obtaining near-optimal solutions for NP-Complete problems like task scheduling. However, most of these algorithms still suffer from inadequate balance between local and global search when seeking a global solution, which often results in sub-optimal solutions. In this paper, an adaptive benefit factors based symbiotic organisms search (ABFSOS) is proposed, that adaptively tune SOS control parameters to strike a balance between local and global search procedures for faster convergence speed. Moreover, an adaptive constrained handling strategy is integrated into the proposed algorithm to effectively tune the values of the penalty function, thereby avoiding infeasible solutions and premature convergence. The performance of the proposed constrained multi-objective ABFSOS (CMABFSOS) was evaluated using large instances of both standard, and synthetic workloads, on a standard toolkit simulator (CloudSim). The non-dominated solutions obtained by the proposed CMABFSOS algorithm outperforms the compared algorithms (EMS-C, and ECMSMOO) for all the workload instances. The proposed CMABFSOS algorithm obtained significant improvement of hypervolume (convergence and diversity) over the compared algorithms for the workload instances. The performance improvement of CMABFSOS over EMS-C ranges from 17.02 to 47.73% across the workloads, while the performance improvement over ECMSMOO is between 19.98 to 52.18%.