Hybrid Whale-Bee Optimization (HWBO) based Optimal Task Offloading Scheme in MCC
M. S. Premalatha1, B. Ramakrishnan2
1M. S. Premalatha, Research Scholar, Manonmanium Sundaranar University, Abishekapatti, Thirunelveli, Tamil Nadu, India.
2Dr. B. Ramakrishnan, Associate Professor, Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamil Nadu, India.
Manuscript received on 01 February 2019 | Revised Manuscript received on 07 February 2019 | Manuscript Published on 13 February 2019 | PP: 281-292 | Volume-8 Issue- 4S February 2019 | Retrieval Number: DS2876028419/2019©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Transferring the tasks from portable gadgets to public cloud is one of the important processes in Mobile Cloud Computing (MCC). Subsequently, offloading differed errands in the meantime will build the ‘cloudlets’ load and enlarges the basic finish time of the offloaded assignments. Storing of tasks in the cloud storage is energy consumed process. The optimal position is to be identified for offloading the tasks from portable gadgets. In order to solve the issue, an optimal task offloading technique is proposed. A hybrid optimization method based on Hybrid Whale Optimization algorithm (WOA) and Artificial Bee colony optimization algorithm (ABC). Dual task assignment process incorporated with queuing models offloads the task in the optimal place of the cloud to reduce the drop rate. The efficiency of the proposed scheme is evaluated with the conventional methods on the basis of energy consumption, drop rate etc.
Keywords: Whale Optimization, Average Response Time, Energy Consumption, Mobile Cloud Computing, Queuing model.
Scope of the Article: Computer Science and Its Applications