Energy Efficient Clustering and Job Scheduling in Mobile Grid Computing Environment
S. Kavitha Bharathi1, M. Dhavamani2
1S. Kavitha Bharathi, Assistant Professor, Department of Computer Applications, India.
2M. Dhavamani, Assistant Professor (Sr.G), Department of Mathematics Kongu Engineering Collge, Perundurai (Tamil Nadu), India.
Manuscript received on 27 March 2019 | Revised Manuscript received on 06 April 2019 | Manuscript Published on 27 April 2019 | PP: 722-729 | Volume-7 Issue-6S2 April 2019 | Retrieval Number: F11230476S219/2019©BEIESP
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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: In the mobile grid environment, job scheduling is the main constituents to resolve the overload of mobile devices. The job scheduling should be energy efficient in the mobile grid so that energy efficient clustering and job scheduling are presented in this paper. Prior to the job scheduling, the grid server selects the Cluster Heads (CHs) based on the maximum capacity of Resource Providers (RPs) or mobile devices in the network. Then the clusters are formed by joining the other RPs with a low latency communication link to the corresponding CHs. After the formation of clusters, the CH schedules the received jobs which are to be processed to the suitable RPs in the network. For this efficient job scheduling, Oppositional Particle Swarm Optimization (OPSO) algorithm is presented. Optimization process of PSO algorithm is enhanced by integrating the Opposite Based Learning (OBL) method. Simulation results show that the performance of the proposed approach is evaluated in terms of energy efficiency and overhead.
Keywords: Mobile Grid Environment, Cluster Formation, Job Scheduling, Oppositional Particle Swarm Optimization, Opposite Based Learning.
Scope of the Article: Clustering