TY - GEN
T1 - Optimal offloading control for a mobile device based on a realistic battery model and semi-Markov decision process
AU - Chen, Shuang
AU - Wang, Yanzhi
AU - Pedram, Massoud
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/1/5
Y1 - 2015/1/5
N2 - Due to the limited battery capacity in mobile devices, the concept of mobile cloud computing (MCC) is proposed where some applications are offloaded from the local device to the cloud for higher energy efficiency. The portion of applications or tasks to be offloaded for remote processing should be judiciously determined. In this paper, the problem of optimal task dispatch, transmission, and execution in the MCC system is considered. Dynamic voltage and frequency scaling (DVFS) is applied to the local mobile processor, whereas the RF transmitter of the mobile device can choose from multiple modulation schemes and bit rates. The power consumptions of the mobile components that cannot be directly controlled, e.g., the touch screen, GPU, audio codec, and I/O ports, are also accounted for through capturing their correlation with the mobile processor and RF transmitter. Finally, a realistic and accurate battery model is adopted in this work in order to estimate the battery energy loss rate in a more accurate way. This paper presents a semi-Markov decision process (SMDP)-based optimization framework, with the actions of different DVFS levels and modulation schemes/transimission bit rates and the objective of minimizing both the energy drawn from the battery and the average latency in request servicing. This paper derives the optimal solution, including the optimal DVFS policy, offloading rate, and transmission scheme, using linear programming combined with a heuristic search. Experiments are conducted on Qualcomm Snapdragon Mobile Development Platform MSM8660 to find the correlations among the power consumptions of the CPU, RF components, and other components. Simulation results show that the proposed optimal solution consistently outperforms some baseline algorithms.
AB - Due to the limited battery capacity in mobile devices, the concept of mobile cloud computing (MCC) is proposed where some applications are offloaded from the local device to the cloud for higher energy efficiency. The portion of applications or tasks to be offloaded for remote processing should be judiciously determined. In this paper, the problem of optimal task dispatch, transmission, and execution in the MCC system is considered. Dynamic voltage and frequency scaling (DVFS) is applied to the local mobile processor, whereas the RF transmitter of the mobile device can choose from multiple modulation schemes and bit rates. The power consumptions of the mobile components that cannot be directly controlled, e.g., the touch screen, GPU, audio codec, and I/O ports, are also accounted for through capturing their correlation with the mobile processor and RF transmitter. Finally, a realistic and accurate battery model is adopted in this work in order to estimate the battery energy loss rate in a more accurate way. This paper presents a semi-Markov decision process (SMDP)-based optimization framework, with the actions of different DVFS levels and modulation schemes/transimission bit rates and the objective of minimizing both the energy drawn from the battery and the average latency in request servicing. This paper derives the optimal solution, including the optimal DVFS policy, offloading rate, and transmission scheme, using linear programming combined with a heuristic search. Experiments are conducted on Qualcomm Snapdragon Mobile Development Platform MSM8660 to find the correlations among the power consumptions of the CPU, RF components, and other components. Simulation results show that the proposed optimal solution consistently outperforms some baseline algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84936888622&partnerID=8YFLogxK
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U2 - 10.1109/ICCAD.2014.7001378
DO - 10.1109/ICCAD.2014.7001378
M3 - Conference contribution
AN - SCOPUS:84936888622
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
SP - 369
EP - 375
BT - 2014 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2014 - Digest of Technical Papers
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 33rd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2014
Y2 - 2 November 2014 through 6 November 2014
ER -