Deep Reinforcement Learning-Based Routing Optimization Algorithm for Edge Data Center
- Resource Type
- Conference
- Authors
- Zhao, Jixin; Zhang, Shukui; Zhang, Yang; Zhang, Li; Long, Hao
- Source
- 2021 IEEE Symposium on Computers and Communications (ISCC) Computers and Communications (ISCC), 2021 IEEE Symposium on. :1-7 Sep, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Data centers
Multi-access edge computing
Simulation
Bandwidth
Reinforcement learning
Routing
Load management
software-defined networking
deep reinforcement learning
routing optimization
edge data center
- Language
- ISSN
- 2642-7389
Mobile Edge Computing (MEC) has solved a sharp increase in data volume caused by various emerging network applications. The edge data center is an essential part of MEC, which connects the edge of the network and the backbone network. Faced with a complex network environment, edge data centers suffer low bandwidth resource utilization and high network latency. This paper proposes Twin Delayed Deep Deterministic policy gradient based Routing Optimization (TRO) algorithm to improve the performance of edge data centers. The TRO algorithm uses Deep Reinforcement Learning (DRL) and Software-Defined Networking (SDN) to achieve routing optimization from two aspects of bandwidth utilization and load balancing. Experiments demonstrate that compared with other algorithms, the TRO algorithm proposed in this paper significantly improves network throughput and reduces average packet latency and average packet latency error.