TY - GEN
T1 - Power-aware wireless transmission for computation offloading in mobile cloud
AU - Zhang, Lei
AU - Zhang, Cong
AU - Liu, Jiangchuan
AU - CHU, Xiaowen
AU - Xu, Ke
AU - Wang, Haiyang
AU - Jiang, Yong
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/14
Y1 - 2016/9/14
N2 - In today's mobile devices, the battery reservoir remains severely limited in capacity, making power consumption a key concern in the design and implementation of mobile applications. In this paper, we closely examine one widely adopted approach to improve the energy efficiency of mobile applications-adaptively offloading the computation to the remote cloud. In particular, we measure the power consumption of computation offloading for two representative real-world mobile cloud applications under various wireless network conditions and identify the unique features of data transmission for computation offloading. We then formulate the power-aware scheduling problem for computation offloading and present a scheduling algorithm that makes adaptive offloading decisions according to the dynamic network conditions. Simulation results show that our proposed method can achieve better battery performance, which also reveal that computation-intensive and delay-tolerant tasks are more likely to benefit from offloading.
AB - In today's mobile devices, the battery reservoir remains severely limited in capacity, making power consumption a key concern in the design and implementation of mobile applications. In this paper, we closely examine one widely adopted approach to improve the energy efficiency of mobile applications-adaptively offloading the computation to the remote cloud. In particular, we measure the power consumption of computation offloading for two representative real-world mobile cloud applications under various wireless network conditions and identify the unique features of data transmission for computation offloading. We then formulate the power-aware scheduling problem for computation offloading and present a scheduling algorithm that makes adaptive offloading decisions according to the dynamic network conditions. Simulation results show that our proposed method can achieve better battery performance, which also reveal that computation-intensive and delay-tolerant tasks are more likely to benefit from offloading.
UR - http://www.scopus.com/inward/record.url?scp=84991782187&partnerID=8YFLogxK
U2 - 10.1109/ICCCN.2016.7568560
DO - 10.1109/ICCCN.2016.7568560
M3 - Conference proceeding
AN - SCOPUS:84991782187
T3 - 2016 25th International Conference on Computer Communications and Networks, ICCCN 2016
BT - 2016 25th International Conference on Computer Communications and Networks, ICCCN 2016
PB - IEEE
T2 - 25th International Conference on Computer Communications and Networks, ICCCN 2016
Y2 - 1 August 2016 through 4 August 2016
ER -