Paper Info
Title
GWOTS: Grey Wolf Optimization Based Task Scheduling at the Green Cloud Data Center
Abstract
Task Scheduling is a key challenging issue of Infrastructure as a Service (IaaS) based cloud data center and it is well-known NP-complete problem. As the number of users' requests increases then the load on the cloud data center will also increase gradually. To manage the heavy load on the cloud data center, in this paper, we propose multiobjective Grey Wolf Optimization (GWO) technique for task scheduling. The main objective of our proposed GWO based scheduling algorithm is to achieve optimum utilization of cloud resources for reducing both the energy consumption of the data center and total makespan of the scheduler for the given list of tasks while providing the services as requested by the users. Our proposed scheduling algorithm is compared with non meta-heuristic algorithms (First-Come-First-Serve (FCFS) and Modified Throttle (MT)), and meta-heuristic algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO)). Experimental results demonstrate that the proposed GWO based scheduler outperforms all algorithms considered for performance evaluation in terms of makespan for the list of tasks, resource utilization and energy consumption.
Year
DOI
Venue
2018
10.1109/SKG.2018.00034
2018 14th International Conference on Semantics, Knowledge and Grids (SKG)
Keywords
Field
DocType
Task analysis,Cloud computing,Data centers,Resource management,Scheduling,Energy consumption,Scheduling algorithms
Particle swarm optimization,Job shop scheduling,Swarm behaviour,Scheduling (computing),Computer science,Energy consumption,Data center,Genetic algorithm,Cloud computing,Distributed computing
Conference
ISSN
ISBN
Citations 
2325-0623
978-1-7281-0441-6
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Natesha B. V.171.79
Neeraj Kumar Sharma2306.79
Shridhar Domanal3132.63
Ram Mohana Reddy Guddeti4488.76