TY - GEN
T1 - Adaptive computation scaling and task offloading in mobile edge computing
AU - Dinh, Thinh Quang
AU - Tang, Jianhua
AU - La, Quang Duy
AU - Quek, Tony Q.S.
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/5/10
Y1 - 2017/5/10
N2 - The energy consumption and applications' execution latency of mobile devices (MDs) can be improved by migrating application tasks to a nearby edge device. In this paper, we propose an optimization framework to investigate the scenario when a MD can offload tasks to multiple access points (APs) and scale its central process unit (CPU) frequency. Firstly, the optimal solution is derived from an exhaustive search based approach; and then a semidefinite relaxation (SDR) based approach is proposed to efficiently solve the problem. The obtained results from our simulation indicate that the SDR-based algorithm is able to achieve close-to-optimal performance. We also show that our proposed scheme can reduce the MD's energy consumption and tasks' execution latency, by taking advantage of having multiple APs and flexible CPU frequency.
AB - The energy consumption and applications' execution latency of mobile devices (MDs) can be improved by migrating application tasks to a nearby edge device. In this paper, we propose an optimization framework to investigate the scenario when a MD can offload tasks to multiple access points (APs) and scale its central process unit (CPU) frequency. Firstly, the optimal solution is derived from an exhaustive search based approach; and then a semidefinite relaxation (SDR) based approach is proposed to efficiently solve the problem. The obtained results from our simulation indicate that the SDR-based algorithm is able to achieve close-to-optimal performance. We also show that our proposed scheme can reduce the MD's energy consumption and tasks' execution latency, by taking advantage of having multiple APs and flexible CPU frequency.
UR - http://www.scopus.com/inward/record.url?scp=85019663028&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019663028&partnerID=8YFLogxK
U2 - 10.1109/WCNC.2017.7925612
DO - 10.1109/WCNC.2017.7925612
M3 - Conference contribution
AN - SCOPUS:85019663028
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017
Y2 - 19 March 2017 through 22 March 2017
ER -