期刊名称：International Journal of Computer Networks and Applications (IJCNA)
电子版ISSN：2395-0455
出版年度：2020
卷号：7
期号：5
页码：137-145
语种：English
出版社：EverScience Publications
摘要：With the advancement in internet technology, everyone can able to utilize resources with low cost using cloud resources. There will be numerous requests for task scheduling to share resources in the cloud environment. When the task request is received by the cloud technology it should have the ability to distribute the workload among sharable resources in a balanced manner and effective utilization of resources. Machine learning and metaheuristic algorithms provide a dynamic part in balanced task assignments in the cloud paradigm. Existing unsupervised models-based load balancing, centroid selection is done randomly and imprecise job requests are not well handled by them. This paper aims to develop a clustering model-based task scheduling with the knowledge of behavioural inspired optimization algorithm in a highly balanced manner. A robust Intuitionistic Fuzzy C-means empowered grasshopper optimization has been anticipated in this work, which utilizes the merits of the Intuitionistic fuzzy and Grass Hopper algorithm for prominent task scheduling among virtual servers in a cloud environment. The results proved that IFCM-GOA reduces the makespan, execution time and, high balance load scheduling with improved cloud resource utilization.
关键词：Task Scheduling;Cloud Computing;Machine Learning;Intuitionistic Fuzzy C Means;Grasshopper Optimization
