CloudPin: A Root Cause Localization Framework of Shared Bandwidth Package Traffic Anomalies in Public Cloud Networks

Due to the sharing nature of public cloud, most of the cloud services use a sharing bandwidth package (sBwp) model to conduct inbound/outbound communication. The sBwp model allows users to purchase a sharing bandwidth for plenty of virtual machines instead of purchasing bandwidth for each virtual machine separately. The advantage of sBwp is that it can provide users with convenient configuration and lower economic cost. However, the sBwp model brings new challenges for operators to localize the root cause of traffic anomalies of a sharing bandwidth, especially for a globally distributed large-scale public cloud with millions of users. In this paper, we first formalize the sBwp problem on the cloud and propose CloudPin, a root cause localization framework for this problem. Our framework solves all the challenges by employing a multi-dimensional algorithm with three sub-models of prediction deviation, anomaly ampli-tude, and shape similarity, and an overall ranking algorithm. Evaluations on real-world data, from one of the world-renowned public cloud vendors, show that our algorithm precision reaches 97.8% for the top 1 of the ranking list, outperforming multiple baseline algorithms.