Dynamic computation offloading in time-varying environment for ultra-dense networks: a stochastic game approach

doi:10.19682/j.cnki.1005-8885.2021.1003

中国邮电高校学报(英文版) ›› 2021, Vol. 28 ›› Issue (2): 24-37.doi: 10.19682/j.cnki.1005-8885.2021.1003

Dynamic computation offloading in time-varying environment for ultra-dense networks: a stochastic game approach

Xie Renchao, Liu Xu, Duan Xuefei, Tang Qinqin, Yu Fei Richard, Huang Tao

1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
2. Guangdong Communications and Networks Institute, Guangzhou 510700, China
3. Department of Future Networks, Purple Mountain Laboratory, Nanjing 211111, China
4. Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada

收稿日期:2020-08-19 修回日期:2021-02-24 出版日期:2021-04-30 发布日期:2021-04-30
通讯作者: Xie Renchao E-mail:renchao_xie@bupt.edu.cn

Dynamic computation offloading in time-varying environment for ultra-dense networks: a stochastic game approach

Xie Renchao, Liu Xu, Duan Xuefei, Tang Qinqin, Yu Fei Richard, Huang Tao

1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
2. Guangdong Communications and Networks Institute, Guangzhou 510700, China
3. Department of Future Networks, Purple Mountain Laboratory, Nanjing 211111, China
4. Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada

Received:2020-08-19 Revised:2021-02-24 Online:2021-04-30 Published:2021-04-30
Contact: Xie Renchao E-mail:renchao_xie@bupt.edu.cn

摘要/Abstract

摘要： To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,
combining ultra-dense networks (UDNs) and mobile edge computing (MEC)are considered as important solutions.
In the MEC enabled UDNs, one of the most important issues is computation offloading. Although a number of work
have been done toward this issue, the problem of dynamic computation offloading in time-varying environment,
especially the dynamic computation offloading problem for multi-user, has not been fully considered. Therefore, in
order to fill this gap, the dynamic computation offloading problem in time-varying environment for multi-user is
considered in this paper. By considering the dynamic changes of channel state and users queue state, the dynamic
computation offloading problem for multi-user is formulated as a stochastic game, which aims to optimize the delay
and packet loss rate of users. To find the optimal solution of the formulated optimization problem, Nash Q-learning
(NQLN) algorithm is proposed which can be quickly converged to a Nash equilibrium solution. Finally, extensive
simulation results are presented to demonstrate the superiority of NQLN algorithm. It is shown that NQLN algorithm
has better optimization performance than the benchmark schemes.

关键词: dynamic computation offloading, time-varying environment, stochastic game, ultra-dense networks (UDNs), mobile edge computing (MEC)

Abstract: To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,
combining ultra-dense networks (UDNs) and mobile edge computing (MEC)are considered as important solutions.
In the MEC enabled UDNs, one of the most important issues is computation offloading. Although a number of work
have been done toward this issue, the problem of dynamic computation offloading in time-varying environment,
especially the dynamic computation offloading problem for multi-user, has not been fully considered. Therefore, in
order to fill this gap, the dynamic computation offloading problem in time-varying environment for multi-user is
considered in this paper. By considering the dynamic changes of channel state and users queue state, the dynamic
computation offloading problem for multi-user is formulated as a stochastic game, which aims to optimize the delay
and packet loss rate of users. To find the optimal solution of the formulated optimization problem, Nash Q-learning
(NQLN) algorithm is proposed which can be quickly converged to a Nash equilibrium solution. Finally, extensive
simulation results are presented to demonstrate the superiority of NQLN algorithm. It is shown that NQLN algorithm
has better optimization performance than the benchmark schemes.

Key words: dynamic computation offloading, time-varying environment, stochastic game, ultra-dense networks (UDNs), mobile edge computing (MEC)

中图分类号:

TN915.01

Xie Renchao, Liu Xu, Duan Xuefei, Tang Qinqin, Yu Fei Richard, Huang Tao. Dynamic computation offloading in time-varying environment for ultra-dense networks: a stochastic game approach[J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(2): 24-37.

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Dynamic computation offloading in time-varying environment for ultra-dense networks: a stochastic game approach

Dynamic computation offloading in time-varying environment for ultra-dense networks: a stochastic game approach

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