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MAGA: A Mobility-Aware Computation Offloading Decision for Distributed Mobile Cloud Computing
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DOI：10.1109/JIOT.2017.2776252.png)
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Abstract
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Distributed mobile cloud computing (MCC) is the new paradigm for providing ubiquitous cloud resources to mobile users with low latency. Mobility is an important factor in distributed MCC which may incur intermittent connectivity and consequently fail computation offloading requests. Latest researches on human mobility show that mobility of users present inherent patterns, periodicity, and predictability. This motivates us to propose a mobile access prediction algorithm based on tail matching subsequence, whose effectiveness and accuracy is validated by experiments using reality mobility dataset. Then MAGA, a mobility-aware offloading decision method for distributed MCC is proposed in this paper for single-job, multicomponent, and multisite offloading scenario. The proposed mobile access prediction is used in MAGA for cloudlet reliability estimation. An integer encoding-based adaptive genetic algorithm is used for offloading decision. Experiment results show the performance advantages of MAGA.
Keywords:
Cloudlet
distributed mobile cloud computing (MCC)
genetic algorithm (GA)
mobility
offloading decision
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