Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64316
Title: Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing
Authors: Guo, ST
Xiao, B 
Yang, YY
Yang, Y
Keywords: Resource allocation
Mobile cloud computing
Energy-efficiency cost
Computation offloadin
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE International Conference on Computer Communications : IEEE INFOCOM 2016, San Francisco, USA, April 12-14, 2016, p. 1-9 How to cite?
Abstract: Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.
URI: http://hdl.handle.net/10397/64316
ISBN: 978-1-4673-9953-1 (electronic)
978-1-4673-9954-8 (Print on Demand(PoD))
DOI: 10.1109/INFOCOM.2016.7524497
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