Guo Yiping, Hu Guyu, Shao Dongsheng
Command and Control Engineering College, People's Liberation Army Engineering University, Nanjing, CO, 210007, China.
Unit 31106 of People's Liberation Army, Nanjing, CO, 210007, China.
Sci Rep. 2022 Aug 26;12(1):14600. doi: 10.1038/s41598-022-18919-w.
According to the research status of Software Defined Network (SDN) control layer traffic scheduling, we find the current common problems, including single path, easy congestion, Quality of Service (QoS) requirements and high delay. To solve these four problems, we design and implement a QoS-oriented global multi-path traffic scheduling algorithm for SDN, referred to as QOGMP. First, we propose a link weight calculation algorithm based on the idea of traction links and deep reinforcement learning, and conduct experimental verifications related to traction links. The algorithm considers QoS requirements and alleviates the problems of easy congestion and high delay. Then, we propose a traffic scheduling algorithm based on link weight and multi-path scheme, which also considers QoS requirements and solves the problem of single path. Finally, we combined the link weight calculation algorithm and the traffic scheduling algorithm to implement QOGMP, and carried out comparative experiments in the built simulation environment. The experimental results show that QOGMP is better than the two comparison algorithms in terms of delay and rescheduling rate.
根据软件定义网络（SDN）控制层流量调度的研究现状，我们发现了当前存在的常见问题，包括单路径、易拥塞、服务质量（QoS）要求以及高延迟。为了解决这四个问题，我们设计并实现了一种面向SDN的全局多路径流量调度算法，称为QOGMP。首先，我们基于牵引链路和深度强化学习的思想提出了一种链路权重计算算法，并进行了与牵引链路相关的实验验证。该算法考虑了QoS要求，缓解了易拥塞和高延迟的问题。然后，我们提出了一种基于链路权重和多路径方案的流量调度算法，该算法也考虑了QoS要求，解决了单路径问题。最后，我们将链路权重计算算法和流量调度算法相结合实现了QOGMP，并在构建的仿真环境中进行了对比实验。实验结果表明，QOGMP在延迟和重调度率方面优于两种对比算法。
