Publication Type : Journal Article
Publisher : Journal of Intelligent and Fuzzy Systems
Source : Journal of Intelligent and Fuzzy Systems, IOS Press, Volume 32, Number 4, p.3081-3089 (2017)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016827450&doi=10.3233%2fJIFS-169251&partnerID=40&md5=a328e417aaccd2960d11b31e74ad2093
Keywords : Application partitioning, Cellular telephone systems, Context information, Context-Aware, decision making, Dynamic decision making, Intelligent systems, Mobile clouds, Mobile Technology, Personal computers, Prediction-based, Semantics, Smartphones, Soft computing, Statistical regression, Telephone sets
Campus : Coimbatore
School : School of Engineering
Department : Electrical and Electronics
Year : 2017
Abstract : Due to the advancement of mobile technology, a large number of computationally intensive applications are created for smart phones. But the limitations of battery and processing power of smart phones are making it inferior to laptops and desktop computers. Mobile Cloud Offloading (MCO) allows the smart phones to offload computationally intensive tasks to the cloud, making it more effective in terms of energy, speed or both. Increased networking capacity due to the availability of high speed Wi-Fi and cellular connections like 3G/4G makes offloading more efficient. Even then, the choice of offloading is not always advisable, because of the highly dynamic context information of mobile devices and clouds. In this paper, we propose a dynamic decision making system, which will decide whether to offload or do the tasks locally, depending on the current context information and the optimization choice of the user. Metrics are developed for time, energy and combination of time and energy to assess the proposed system. A test bed is implemented and the results are showing improvements from the currently existing methods. © 2017-IOS Press and the authors. All rights reserved.
Cite this Research Publication : Dhanya N. M., Kousalya, G., and Balakrishnan P., “Dynamic mobile cloud offloading prediction based on statistical regression”, Journal of Intelligent and Fuzzy Systems, vol. 32, pp. 3081-3089, 2017.