Abstract
The objective of mobile cloud computing (MCC) is to augment the computation resources of mobile devices to reduce energy consumption of the device and utilization of high computation resources in the cloud. In an MCC framework a mobile device opportunistically offloads some of its computation tasks to remote cloud infrastructure in order to reduce its energy consumption. The scheme is sensitive to communication bandwidth, since low bandwidth implies longer duration that the network card remains active for and therefore consumes higher energy. To mitigate this problem, systems like MAUI, periodically updates its offloading strategy. But execution of such strategy is also associated with some cost in form of computation or energy. In this paper we present some on-line algorithms, which are computationally less costly, yet perform same as MAUI’s optimizer. We found experimentally that if we augment our proposed offloading algorithms with mobility model of the device in a WiFi covered area, we do not achieve any significant gain in terms of saving energy of the device.
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Paul, H.S., Datta, P., Banerjee, A., Mukherjee, A. (2015). Compute on the Go: A Case of Mobile-Cloud Collaborative Computing Under Mobility. In: Pop, F., Potop-Butucaru, M. (eds) Adaptive Resource Management and Scheduling for Cloud Computing. ARMS-CC 2015. Lecture Notes in Computer Science(), vol 9438. Springer, Cham. https://doi.org/10.1007/978-3-319-28448-4_6
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DOI: https://doi.org/10.1007/978-3-319-28448-4_6
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