Bivariate Correlative Oppositional Based Artificial Fish Swarm Resource Optimized Task Scheduling In Cloud

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Published Jul 1, 2020
https://doi.org/10.47164/ijngc.v11i2.174
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Volume 11, Issue 2, July 2020

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AJITHA K M
Research Scholar,Reg No: 19123152282020 ,S.T.Hindu College ,Nagercoil. Affiliation under Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli-627012, TamilNadu, India.
Dr. N. Chenthalir Indra
Research Supervisor and Assistant Professor, S.T Hindu College ,Nagercoil .Affiliation under Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli-627012, TamilNadu, India.

Abstract

Cloud computing is an Internet-based approach provisioning of various computing services, to the users. In cloud, task scheduling is a significant process to allocate the workload for the different servers. Different evolutionary algorithms have been designed to solve the task scheduling issues in the cloud. But, the makespan and resource utilization performance were not improved during task scheduling by using population-based algorithms. In order to improve the task scheduling efficiency with minimum makespan and resource utilization, Bivariate Correlative Oppositional based Multiobjective Artificial Fish Swarm Resource Optimized Task Scheduling (BCO-MAFSROTS) technique is introduced. The main objective of the BCO-MAFSROTS technique is to reduce the workload across the cloud server by distributing the number of user-requested tasks to the optimal virtual machines. Initially, the number of user tasks are taken from the database. Then, incoming user tasks is given to the cloud sever. In the cloud server, bivariate correlation-based tasks prioritization method is performed for prioritized task as a high priority and low priority. Based on the prioritization, user tasks are distributed to the optimal virtual machines with the help of optimization algorithm. In the proposed BCO-MAFSROTS technique, oppositional based multiobjective artificial fish swarm optimization algorithm is utilized to perform task scheduling according to identifying optimal virtual machines in the cloud. The fitness is determined for each virtual machines based on multiple objective functions such as CPU time, bandwidth, memory and energy. From the fitness function estimation cloud server finds the optimal virtual machines for processing user-request task. Next, the experimental evaluation is carried out on factors such as task scheduling efficiency, false-positive rate, makespan and memory consumption with respect to a number of user tasks. The results discussion proves that the presented BCO-MAFSROTS technique improves the task scheduling efficiency and minimizes false-positive rate, makespan as well as memory consumption as compared to state-of-the-art methods.

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How to Cite
AJITHA K M, & Dr. N. Chenthalir Indra. (2020). Bivariate Correlative Oppositional Based Artificial Fish Swarm Resource Optimized Task Scheduling In Cloud. International Journal of Next-Generation Computing, 11(2), 163–177. https://doi.org/10.47164/ijngc.v11i2.174
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