TY - JOUR
T1 - Task Offloading and Scheduling in Fog RAN
T2 - A Parallel Communication and Computation Perspective
AU - Guo, Kun
AU - Sheng, Min
AU - Quek, Tony Q.S.
AU - Qiu, Zhiliang
N1 - Funding Information:
Manuscript received September 1, 2019; accepted October 17, 2019. Date of publication October 22, 2019; date of current version February 7, 2020. This work was supported in part by the Natural Science Foundation of China under Grant 61725103 and Grant 91638202, in part by Shaanxi Innovation Group 2017KCT-30-03, in part by the Key Industry Innovation Chain of Shaanxi under Grant 2017ZDCXL-GY-04-04, and in part by the National S&T Major Project under Grant 2018ZX03001014-004. The associate editor coordinating the review of this article and approving it for publication was M. A. Assaad. (Corresponding author: Min Sheng.) K. Guo, M. Sheng, and Z. Qiu are with the State Key Laboratory of ISN, Xidian University, Xi’an 710071, China (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2012 IEEE.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - Due to the parallel computing at tiered computing nodes, fog radio access network (RAN) can facilitate computation offloading. Apart from the parallel computing, communication and computation operations can be conducted simultaneously at tiered computing nodes. In this regard, both the set and the order of tasks operated at each computing node have a significant impact on the task execution delay. Hence, we jointly optimize task offloading and scheduling to minimize the average task execution delay in fog RAN, followed by an effective recursive algorithm. Through application extensions and numerical evaluations, the proposed algorithm is verified with scalability and efficacy.
AB - Due to the parallel computing at tiered computing nodes, fog radio access network (RAN) can facilitate computation offloading. Apart from the parallel computing, communication and computation operations can be conducted simultaneously at tiered computing nodes. In this regard, both the set and the order of tasks operated at each computing node have a significant impact on the task execution delay. Hence, we jointly optimize task offloading and scheduling to minimize the average task execution delay in fog RAN, followed by an effective recursive algorithm. Through application extensions and numerical evaluations, the proposed algorithm is verified with scalability and efficacy.
UR - https://www.scopus.com/pages/publications/85079660991
UR - https://www.scopus.com/pages/publications/85079660991#tab=citedBy
U2 - 10.1109/LWC.2019.2948860
DO - 10.1109/LWC.2019.2948860
M3 - Article
AN - SCOPUS:85079660991
SN - 2162-2337
VL - 9
SP - 215
EP - 218
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
IS - 2
M1 - 8879574
ER -