Fault Diagnosis for the Virtualized Network in the Cloud Environment using Reinforcement Learning
Tao Xu, Dian Shen, Huanhuan Zhang, Runqun Xiong, Jiahui Jin · 2019
In the cloud environment, the virtualized network provides the connectivity to a massive of virtual machines through various virtual network devices. In such a complicated networking system, network faults are not occasional. It is urging for the system administrators to have the ability to investigate a fault and recover from it. However, the complexity of the virtualized network and the similarity among the symptom of faults makes the accurate diagnosis challenging. In this paper, we leverage the method of reinforcement learning to facilitate the fault diagnosis in the cloud environment, where it diagnoses the faults through an “exploration and exploitation” manner. Further, we investigate the key factors that influence the network performance and may cause the network faults. Based on this investigation, we present how to train the network diagnosis module with the Q-learning algorithm. Experimental results show that the diagnosis accuracy of our reinforcement learning based method is around 8% higher than traditional methods, and incurs very slight system overhead.