Affiliations: [a] Department of Computer Science and Engineering, Research Scholar, ANU College of Engineering, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India
| [b] Department of Computer Science and Engineering, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India
Correspondence:
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Corresponding author. Karnam Sreenu, Research Scholar, Department of Computer Science and Engineering, ANU College of Engineering, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India. E-mail: karnam.sreenu@sreenidhi.edu.in.
Abstract: Task scheduling on heterogeneous data centers of cloud computing environment is a challenging problem. The efficiency of the cloud depends on the adopted task scheduling strategy. The task scheduling algorithm schedules the required task resources of application in the cloud platform. Even though many algorithms are presented for task scheduling; they consider only the minimum objectives for the trade-off of optimal scheduling. In this paper, a multi-objective task scheduling strategy is proposed for the task scheduling problem in the cloud network as an NP-hard optimization problem. In order to solve the scheduling problem, Fractional Grey wolf Multi-objective optimization-based Task Scheduling strategy (FGMTS) is newly proposed for scheduling tasks in the cloud. The proposed FGMTS algorithm is the combination of the existing fractional theory and Grey Wolf Optimizer algorithm. Also, the multi-objective function is newly formulated to solve the multi-objective scheduling problem. The fitness function for the proposed optimization considers the parameters, such as Execution time, Communication time, Execution cost, Communication cost, Energy, and Resource utilization for optimal scheduling. The experimentation of the proposed task scheduling strategy is carried out over two cloud setups. The performance of proposed system is validated over the existing techniques, such as PSO, GA, and GWO using the metrics considered in the multi-objective formulation function. The experimental results show that the proposed FGMTS-Task scheduling scheme allocates the resource for all incoming task requests while preserving the performance of the cloud with an increase in the profit.
