Research Area: Cloud Computing
In cloud computing, efficient task scheduling espouses many challenges. To schedule the multiple cloudlets with deadline constraints on hybrid cloud resources while meeting the various quality requirements is a challenging issue. The purpose of this research work is to address the task scheduling problem of cloud computing. A novel hybrid task scheduling algorithm named Chemical Reaction Partial Swarm Optimization has been proposed for the allotment of multiple independent tasks on the available virtual machines. It enhances the classical chemical reaction optimization and partial swarm optimization and does hybridization by combining the features for the optimal schedule sequence where tasks can be processed based upon the demand and deadline simultaneously to improve the quality in terms of factors like cost, energy, and makespan. We present the comprehensive simulation experiment using the CloudSim toolkit, which shows the effectiveness of the proposed algorithms. To analyse average execution time, comparative experiments have been carried out using various combinations of virtual machines and the number of tasks. The results bring out a significant reduction in execution time of the order of 1–6 percent, which further improves even more than 10 percent in some cases. The results of the makespan reflect the effectiveness of the algorithm in order of 5–12 percent, and the outcome of total cost 2–10 percent and energy consumption rate shows the 1–9 percent improvement.
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Author(s) Name: KalkaDubey,S.C.Sharma
Journal name: Sustainable Computing: Informatics and Systems
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Publisher name: Elsevier
DOI: 10.1016/j.suscom.2021.100605
Volume Information: Volume 32, December 2021, 100605
