Regressive Whale Optimization for Workflow Scheduling in Cloud Computing

Abstract

Cloud computing is the advancing technology that aims at providing services to the customers with the available resources in the cloud environment. When the multiple users request service from the cloud server, there is a need of the proper scheduling of the resources to attain good customer satisfaction. Therefore, this paper proposes the Regressive Whale Optimization (RWO) algorithm for workflow scheduling in the cloud computing environment. RWO is the meta-heuristic algorithm, which schedules the task depending on a fitness function. Here, the fitness function is defined based on three major constraints, such as resource utilization, energy, and the Quality of Service (QoS). Therefore, the proposed task scheduling requires minimum time and cost for executing the task in the virtual machines. The performance of the proposed method is analyzed using the four experimental setups, and the results of the analysis prove that the proposed multi-objective task scheduling method performs well than the existing methods. The evaluation metrics considered for analyzing the performance of the proposed workflow scheduling method are resource utilization, energy, cost, and time. Resource utilization is the process of making the most of the resources available for performing tasks. Energy is the quantitative property of the resource to perform tasks. The proposed method attains the maximum resource utilization at a rate of 0.0334, minimal rate of energy, scheduling cost, and time as 0.2291, 0.0181, and 0.0007, respectively.