Journal
IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume 18, Issue 2, Pages 319-333Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2018.2831230
Keywords
Mobile cloud computing; energy-efficiency cost; computation offloading; resource allocation
Funding
- US National Science Foundation [CSR 1513719]
- National Natural Science Foundation of China [61772432, 61772433, 61772446]
- Fundamental Research Funds for the Central Universities [XDJK2015C010, XDJK2015C019, XDJK2015D023, XDJK2016A011, XDJK2016D047, XDJK201710635069]
- Natural Science Key Foundation of Chongqing [cstc2015jcyjBX0094]
- Tianjin Key Laboratory of Advanced Networking (TANK), School of Computer Science and Technology, Tianjin University, Tianjin China
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Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy for smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto resource-rich cloud. However, how to achieve energy-efficient computation offloading under hard constraint for application completion time remains a challenge. 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 an energy-efficiency cost (EEC) minimization problem while satisfying task-dependency requirement and completion time deadline constraint. We then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control, and transmission power allocation. Next, we show that 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, we provide experimental results in a real testbed and demonstrate that the eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting the transmission power for wireless channel conditions in cloud computing.
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