Abstract
In this paper, we study the problem of opportunistic task scheduling and workload management in a mobile cloud setting considering computation power variation. We gathered mobile usage data for a number of persons and applied supervised clustering to show that a pattern of usage exists and that follows a state-based model. Based on this model, we present a strategy to choose and offload work on a mobile device. We present a framework and experimental results showing the efficacy of our proposed approach.
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Banerjee, A., Paul, H.S., Mukherjee, A., Dey, S., Datta, P. (2014). A Framework for Speculative Scheduling and Device Selection for Task Execution on a Mobile Cloud. In: Pop, F., Potop-Butucaru, M. (eds) Adaptive Resource Management and Scheduling for Cloud Computing. ARMS-CC 2014. Lecture Notes in Computer Science(), vol 8907. Springer, Cham. https://doi.org/10.1007/978-3-319-13464-2_4
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DOI: https://doi.org/10.1007/978-3-319-13464-2_4
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