延迟(音频)
边缘计算
低延迟(资本市场)
分布式计算
蜂窝网络
调度(生产过程)
服务质量
作者
Wen Sun,Haibin Zhang,Rong Wang,Yan Zhang
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2020-10-01
卷期号:69 (10): 12240-12251
被引量:129
标识
DOI:10.1109/tvt.2020.3018817
摘要
6G is envisioned to empower wireless communication and computation through the digitalization and connectivity of everything, by establishing a digital representation of the real network environment. Mobile edge computing (MEC), as one of the key enabling factors, meets unprecedented challenges during mobile offloading due to the extremely complicated and unpredictable network environment in 6G. The existing works on offloading in MEC mainly ignore the effects of user mobility and the unpredictable MEC environment. In this paper, we present a new vision of Digital Twin Edge Networks (DITEN) where digital twins (DTs) of edge servers estimate edge servers' states and DT of the entire MEC system provides training data for offloading decision. A mobile offloading scheme is proposed in DITEN to minimize the offloading latency under the constraints of accumulated consumed service migration cost during user mobility. The Lyapunov optimization method is leveraged to simplify the long-term migration cost constraint to a multi-objective dynamic optimization problem, which is then solved by Actor-Critic deep reinforcement learning. Simulations results show that our proposed scheme effectively diminishes the average offloading latency, the offloading failure rate, and the service migration rate, as compared with benchmark schemes, while saving the system cost with DT assistance.
科研通智能强力驱动
Strongly Powered by AbleSci AI