Machine Learning based Root Cause Analysis for SDN Network

Nowadays, the rapid growth of the Internet makes network management more complex due to various and complicated network problems. In the past, network administrators implemented troubleshooting approaches (e.g., ping, traceroute, etc.) manually to identify the root cause of problems. However, it is not effective due to human intervention and an increase of network devices. Consequently, the root cause analysis is considered by the research community. There are existing studies for the root cause analysis without human intervention (e.g., statistical approaches, heuristic algorithms, etc.). However, these approaches show limited performance (e.g., due to complex threshold identifcation, etc.). The emerging of machine learning (ML) and deep learning is a potential solution to overcome this obstacle, offering an opportunity to develop an effective root cause analysis approach. Therefore, in this paper, we propose a root cause analysis approach using ML and time-series network parameters to identify the root cause of problems in the network. In this approach, we consider balancing the accuracy and the time complexity of ML algorithms to select an appropriate ML technique. Moreover, we contribute troubleshooting datasets to identify three kinds of root causes including link failure, switch failure and buffer overload. The experimental results show that the proposal can achieve approximately 97 percent of precision, recall and f1-score in considered scenarios and require less processing time (only require 0.00143 ms for a sample) in comparison with other ML algorithms.