TY - GEN
T1 - Multi-objective resource allocation for mobile edge computing systems
AU - Zhang, Xinyi
AU - Mao, Yuyi
AU - Zhang, Jun
AU - Letaief, Khaled B.
PY - 2018/2/14
Y1 - 2018/2/14
N2 - To enhance the computation capability of mobile devices by offloading computation-demanding tasks to the nearby edge servers. In order to minimize the task latency and the device energy consumption, in this paper, we investigate the multi-objective resource allocation for multi-user MEC systems by adopting the system utility as the performance metric, which is a normalized weighted combination of the time and energy saving achieved by computation offloading. To provide an efficient solution, a low-complexity ranking-based algorithm is proposed based on the modified Newton method and the concept of computation offloading priority. Simulation results show that our proposed algorithm achieves a near-optimal performance and greatly outperforms a baseline algorithm with random spectrum allocation. In addition, it is demonstrated that jointly optimizing the spectrum and computational resource management policy is more critical when the number of MEC users is large.
AB - To enhance the computation capability of mobile devices by offloading computation-demanding tasks to the nearby edge servers. In order to minimize the task latency and the device energy consumption, in this paper, we investigate the multi-objective resource allocation for multi-user MEC systems by adopting the system utility as the performance metric, which is a normalized weighted combination of the time and energy saving achieved by computation offloading. To provide an efficient solution, a low-complexity ranking-based algorithm is proposed based on the modified Newton method and the concept of computation offloading priority. Simulation results show that our proposed algorithm achieves a near-optimal performance and greatly outperforms a baseline algorithm with random spectrum allocation. In addition, it is demonstrated that jointly optimizing the spectrum and computational resource management policy is more critical when the number of MEC users is large.
KW - Computation offloading
KW - Mobile edge computing
KW - Modified Newton method
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85045275123&partnerID=8YFLogxK
U2 - 10.1109/PIMRC.2017.8292379
DO - 10.1109/PIMRC.2017.8292379
M3 - Conference article published in proceeding or book
AN - SCOPUS:85045275123
T3 - IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
SP - 1
EP - 5
BT - 2017 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017
Y2 - 8 October 2017 through 13 October 2017
ER -