Automated Inference System for End-To-End Diagnosis of Network Performance Issues in Client-Terminal Devices
Traditional network diagnosis methods of Client-Terminal Device (CTD) problems tend to be labor intensive, time consuming, and contribute to increased customer dissatisfaction. In this paper, the authors propose an automated solution for rapidly diagnose the root causes of network performance issues in CTD. Based on a new intelligent inference technique, they create the Intelligent Automated Client Diagnostic (IACD) system, which only relies on collection of Transmission Control Protocol (TCP) packet traces. Using soft-margin Support Vector Machine (SVM) classifiers, the system distinguishes link problems from client problems and identifies characteristics unique to the specific fault to report the root cause.