Research Article
A DQN-Based Approach for Online Service Placement in Mobile Edge Computing
@INPROCEEDINGS{10.1007/978-3-030-67540-0_10, author={Xiaogan Jie and Tong Liu and Honghao Gao and Chenhong Cao and Peng Wang and Weiqin Tong}, title={A DQN-Based Approach for Online Service Placement in Mobile Edge Computing}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2021}, month={1}, keywords={Mobile edge computing Service placement Deep reinforcement learning}, doi={10.1007/978-3-030-67540-0_10} }- Xiaogan Jie
Tong Liu
Honghao Gao
Chenhong Cao
Peng Wang
Weiqin Tong
Year: 2021
A DQN-Based Approach for Online Service Placement in Mobile Edge Computing
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-67540-0_10
Abstract
Due to the development of 5G networks, computation intensive applications on mobile devices have emerged, such as augmented reality and video stream analysis. Mobile edge computing is put forward as a new computing paradigm, to meet the low-latency requirements of applications, by moving services from the cloud to the network edge like base stations. Due to the limited storage space and computing capacity of an edge server, service placement is an important issue, determining which services are deployed at edge to serve corresponding tasks. The problem becomes particularly complicated, with considering the stochastic arrivals of tasks, the additional latency incurred by service migration, and the time spent for waiting in queues for processing at edge. Benefiting from reinforcement learning, we propose a deep Q network based approach, by formulating service placement as a Markov decision process. Real-time service placement strategies are output, to minimize the total latency of arrived tasks in a long term. Extensive simulation results demonstrate that our approach works effectively.
