TY - GEN
T1 - Joint Radio and Computation Resource Management for Low Latency Mobile Edge Computing
AU - Liu, Qiang
AU - Han, Tao
AU - Ansari, Nirwan
N1 - Funding Information:
This work is partially supported by the U.S. National Science Foundation under Grant No. 1731675, No. 1810174 and the UNC-Charlotte Faculty Research Grant.
Publisher Copyright:
© 2018 IEEE.
PY - 2018
Y1 - 2018
N2 - Mobile edge computing (MEC) is a new networking paradigm that enables low-latency computation offloading for compute-intensive mobile applications. The dynamic wireless channel, non-uniform spatiotemporal traffic, and limited computation resources impair the service latency of mobile edge computing. Therefore, jointly managing radio and computation resources is needed to achieve low latency MEC. In this paper, we propose a joint radio and computation resource management (iRAR) algorithm which minimizes users' service latency by optimizing the uplink transmission power, receive beamforming, computation task assignment, and computation resource allocation. We compare the performance of the proposed algorithm with three different algorithms and demonstrate that the iRAR algorithm reduces up to 52% average service latency as compared to the other algorithms.
AB - Mobile edge computing (MEC) is a new networking paradigm that enables low-latency computation offloading for compute-intensive mobile applications. The dynamic wireless channel, non-uniform spatiotemporal traffic, and limited computation resources impair the service latency of mobile edge computing. Therefore, jointly managing radio and computation resources is needed to achieve low latency MEC. In this paper, we propose a joint radio and computation resource management (iRAR) algorithm which minimizes users' service latency by optimizing the uplink transmission power, receive beamforming, computation task assignment, and computation resource allocation. We compare the performance of the proposed algorithm with three different algorithms and demonstrate that the iRAR algorithm reduces up to 52% average service latency as compared to the other algorithms.
KW - Mobile edge computing
KW - beamfoming
KW - computation offloading
KW - task assignment
UR - http://www.scopus.com/inward/record.url?scp=85063464010&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063464010&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2018.8647792
DO - 10.1109/GLOCOM.2018.8647792
M3 - Conference contribution
AN - SCOPUS:85063464010
T3 - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
BT - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Global Communications Conference, GLOBECOM 2018
Y2 - 9 December 2018 through 13 December 2018
ER -