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Learning Based Resource Management in Mobile Cloud Computing


Nowadays, the mobile devices such as smartphones, tablets are playing more and more important roles in our life. Due to some resource limitations such as storage capacity, battery lifetime, or processing ability, the smart mobile device is consuming much more energy than before. According to recent researches, mobile cloud computing is becoming a promising approach, and the core of the mobile cloud computing is computational offloading. In this thesis, I start from choosing different key features to make better prediction by Support Vector Machine (SVM) method to identify if a task is suitable for offloading, then I simulate three cases to calculate energy consumption, time cost, and energy saving rate and make the appropriate decision based on the trade-off between the energy consumption and time cost with the help of SVM.

Keywords: Mobile Cloud Computing, Computational Offloading, Support Vector Machine (SVM).

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