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Scheduling of tasks in a cloud environment has larger influence on time and energy depletion. Different heuristic models were developed to solve the NP-hard task scheduling problem based on time. However, ideal task scheduling algorithms must also maximize energy efficiency with good load balancing and ensure better Quality-of-Service (QoS). An innovative multi-objective Chaotic Darwinian Chicken Swarm Optimization (CDCSO) system is suggested in this article to provide energy efficient QoS and load balancing aware task scheduling. The multi-objective CDCSO algorithm incorporates the chaotic and Darwinian Theory to the standard Chicken Swarm Optimization to increase its global exploration and maximize the convergence rate. This performance enhanced CDCSO algorithm models the cloud task scheduling problem as NP-hard and utilizes the optimization principles to solve them based on multiple objective parameters. The multi-objective fitness function used in CDCSO is modelled based on the objective parameters namely energy, cost, task completion time, response time, throughput and load balancing index. Based on this multi-objective function, the CDCSO effectively allocates the tasks to the suitable energy efficient, cost and time minimized Virtual machines (VMs) which are also optimally load balanced. CloudSim simulations were conducted and the obtained results illustrated that the proposed multi-objective CDCSO has provided better task scheduling with minimized energy, cost, time and optimal load balancing.

Keywords

Cloud Task Scheduling, Multi-Objective Problem, Chaotic Darwinian Chicken Swarm Optimization, Darwinian Theory, Energy Efficiency, Load Balancing Index, Quality-of-Service.

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References

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Robust Resource Scheduling With Optimized Load Balancing Using Grasshopper Behavior Empowered Intuitionistic Fuzzy Clustering in Cloud Paradigm

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Authors

G. Kiruthiga ¹, S. Mary Vennila ¹

Affiliations
1 PG and Research Department of Computer Science, Presidency College, Chennai, Tamil Nadu, IN

Source

International Journal of Computer Networks and Applications, Vol 7, No 5 (2020), Pagination: 137-145

Abstract

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.

Keywords

Task Scheduling, Cloud Computing, Machine Learning, Intuitionistic Fuzzy C Means, Grasshopper Optimization.

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