Hybrid Optimization EHO-GA for Task scheduling in Cloud Environments
K. Loheswaran1, D. Palanivel Rajan2, P. Divya3
1Dr. K.Loheswaran, Associate Professor, Department of Computer Science and Engineering, CMR College of Engineering & Technology, Hyderabad (Telangana) India.
2Dr. D.Palanivel Rajan, Professor, Department of Computer Science and Engineering, CMR College of Engineering, Hyderabad, (Telangana) India.
3P. Divya, Assistant Professor, Department of Computer Science and Engineering, Coimbatore Institute of Engineering & Technology, Coimbatore, (Tamilnadu) India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2569-2573 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8737088619/2019©BEIESP | DOI: 10.35940/ijeat.F8737.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cloud computing is an emerging technology with highly scalable service adopted by different kinds of people from around the world. In cloud environments one of the major problems is task scheduling; most of existing algorithm is not optimal. The proposed hybrid optimization method has combination of Elephant Herd Optimization (EHO) and Genetic Algorithm (GA) for find an optimal resource to schedule task in the Cloud. This proposed method has improves the performance of task scheduling by considering the parameters of response time, makespan, and cost of the cloud. The proposed method has implemented in CloudSim 3.0 toolkit and evaluated the performance with existing algorithm. The Experimental results were proven that proposed algorithm has given better performance compared to other scheduling algorithm.
Keywords: Cloud Computing, Elephant Herd Optimization, Task Scheduling, Genetic Algorithm.