TY - JOUR
T1 - Computation offloading in C-RAN
T2 - 2019 IEEE Global Communications Conference, GLOBECOM 2019
AU - Guo, Kun
AU - Sheng, Min
AU - He, Lijun
AU - Quek, Tony Q.S.
AU - Qiu, Zhiliang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019
Y1 - 2019
N2 - In cloud radio access network (C-RAN), computation-intensive tasks can be offloaded from mobile devices (MDs) to the powerful computing node in C-RAN, i.e., baseband unit (BBU) pool, through cooperation radio at remote radio heads (RRHs), for effective task processing and improved user experience. In the existing works, computational resources in the BBU pool are always allocated to MDs exclusively, resulting in poor resource utilization and deteriorative task processing delay. Alternatively, we adopt a sequential computation model to enhance computing performance, which is proved through theoretical analyses in this paper. In this model, a task scheduling issue should be addressed in the BBU pool to determine the optimal processing order for tasks. Then, one task's completion time is jointly determined by its scheduling order and arrival time in the BBU pool. Hence, to minimize the maximum task completion time, we jointly optimize cooperative radio at RRHs and task scheduling in the BBU pool. By leveraging the specific property of formulated problem, we propose an effective computation offloading algorithm to achieve a local optimal solution in block coordinate descent manner. Finally, simulation results present the convergence and advantage of our proposed algorithm.
AB - In cloud radio access network (C-RAN), computation-intensive tasks can be offloaded from mobile devices (MDs) to the powerful computing node in C-RAN, i.e., baseband unit (BBU) pool, through cooperation radio at remote radio heads (RRHs), for effective task processing and improved user experience. In the existing works, computational resources in the BBU pool are always allocated to MDs exclusively, resulting in poor resource utilization and deteriorative task processing delay. Alternatively, we adopt a sequential computation model to enhance computing performance, which is proved through theoretical analyses in this paper. In this model, a task scheduling issue should be addressed in the BBU pool to determine the optimal processing order for tasks. Then, one task's completion time is jointly determined by its scheduling order and arrival time in the BBU pool. Hence, to minimize the maximum task completion time, we jointly optimize cooperative radio at RRHs and task scheduling in the BBU pool. By leveraging the specific property of formulated problem, we propose an effective computation offloading algorithm to achieve a local optimal solution in block coordinate descent manner. Finally, simulation results present the convergence and advantage of our proposed algorithm.
UR - https://www.scopus.com/pages/publications/85081985736
U2 - 10.1109/GLOBECOM38437.2019.9013857
DO - 10.1109/GLOBECOM38437.2019.9013857
M3 - 会议文章
AN - SCOPUS:85081985736
SN - 2334-0983
JO - Proceedings - IEEE Global Communications Conference, GLOBECOM
JF - Proceedings - IEEE Global Communications Conference, GLOBECOM
M1 - 9013857
Y2 - 9 December 2019 through 13 December 2019
ER -