Crow–penguin optimizer for multiobjective task scheduling strategy in cloud computing

Abstract

SummaryTask scheduling in the cloud is the multiobjective optimization problem, and most of the task scheduling problems fail to offer an effective trade‐off between the load, resource utilization, makespan, and Quality of Service (QoS). To bring a balance in the trade‐off, this paper proposes a method, termed as crow–penguin optimizer for multiobjective task scheduling strategy in cloud computing (CPO‐MTS). The proposed algorithm decides the optimal execution of the available tasks in the available cloud resources in minimal time. The proposed algorithm is the fusion of the Crow Search optimization Algorithm (CSA) and the Penguin Search Optimization Algorithm (PeSOA), and the optimal allocation of the tasks depends on the newly designed optimization algorithm. The proposed algorithm exhibits a better convergence rate and converges to the global optimal solution rather than the local optima. The formulation of the multiobjectives aims at a maximum value through attaining the maximum QoS and resource utilization and minimum load and makespan, respectively. The experimentation is performed using three setups, and the analysis proves that the method attained a better QoS, makespan, Resource Utilization Cost (RUC), and load at a rate of 0.4729, 0.0432, 0.0394, and 0.0298, respectively.