TSMGWO: Optimizing Task Schedule Using Multi-Objectives Grey Wolf Optimizer for Cloud Data Centers

Abstract

Cloud computing is considered to be the best solution for addressing the increasing computing requirements of high-performance applications. The efficient performance of the system requires optimal mapping of cloud tasks to resources. However, it is challenging to address computing and storage requirements of high-performance applications while achieving conflicting scheduling objectives like throughput, makespan, resource utilization. This work proposes a metaheuristic approach called task schedule using a multi-objective grey wolf optimizer (TSMGWO) to find near-optimal task scheduling solutions while handling conflicting objectives. The TSMGWO approach has been evaluated using three benchmark datasets, namely, GoCJ, HCSP and Synthetic dataset. The results are compared with heuristic FCFS and MT methods, and metaheuristic methods PSO, GA and WOA. The TSMGWO approach reduces makespan upto 67.52% over FCFS method, 60.93%, over PSO, 38.05% over GA, and 23.22% over WOA methods for 100 tasked cloud workload using GoCJ dataset. It reduces makespan upto 60.95% over FCFS method, 55.79% over MT method, 47.04% over PSO, 33.38% over GA and 19.91% over WOA method using synthetic dataset. Similarly, TSMGWO reduces makespan upto 27.03% over MT method, 18.95% over PSO, 11.90% over GA and 7.5% over WOA method using HSCP workload. The comparative analysis demonstrates that TSMGWO approach outperforms the earlier heuristic and metaheuristic methods using benchmark datasets in the cloud environment.