Journal of ISMAC
IRO Journals
Volume 2 — Issue 1 — March 2020

Improved Response Time and Energy Management for Mobile Cloud Computing Using Computational Offloading

https://doi.org/10.36548/jismac.2020.1.004
Pages: 38-49
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Jennifer S. Raj
Professor, Department of ECE, Gnanamani College of Technology,
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How to Cite

Raj, Jennifer S. 2020. “Improved Response Time and Energy Management for Mobile Cloud Computing Using Computational Offloading”. Journal of ISMAC 2 (1): 38-49. https://doi.org/10.36548/jismac.2020.1.004.
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Keywords

— Mobile Cloud Computing
— Computational Offloading
— Response Time
— Energy Consumption
— Fuzzy KNN
— Hidden Markov Model and Ant colony optimization
Published: 20-03-2020

Abstract

The mobile devices capabilities are found to be greater than before by utilizing the cloud services. There are various of service rendered by the cloud paradigm and the mobile devices usually allows the execution of the resource-intensive applications on the resource- constrained mobile device to be offloaded to the cloudlets that are resource rich thus enhancing the its processing capabilities. But accessing the cloud services within the minimum response time and energy consumption still remains as a serious research problem. So the proposed method put forth in the paper scopes in developing a frame work to choose the optimal cloud service provider. The frame work proposed is categorized into two stages where the initial stage engages the classifier to segregate the mobile device according to the fuzzy K-nearest neighbor and cultivates an improved computational offloading employing the Hidden Markov Model and ACO- ant colony optimization. The algorithm proffered is implemented in the MATLAB version 9.1 and the performance is evinced on the basis of the response time, energy consumption and the processing cost. The results obtained through the proposed method proves to provide an 89% better response time, 95 % better energy consumption and 50% enhanced processing cost compared to the few existing computational offloading methods put forth for the mobile cloud computing.

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