Automated Inference System for End-To-End Diagnosis of Network Performance Issues in Client-Terminal Devices

TL;DR

This paper introduces IACD, an automated system that uses TCP packet traces and SVM classifiers to rapidly diagnose network issues in client devices with high accuracy, reducing manual effort and improving customer satisfaction.

Contribution

The paper presents a novel intelligent inference system that automates network diagnosis in client devices using TCP traces and machine learning, supporting diverse fault types and device independence.

Findings

Achieved 98% accuracy in fault diagnosis

Effectively distinguishes link problems from client issues

Supports new access link and fault types

Abstract

Traditional network diagnosis methods of Client-Terminal Device (CTD) problems tend to be laborintensive, time consuming, and contribute to increased customer dissatisfaction. In this paper, we propose an automated solution for rapidly diagnose the root causes of network performance issues in CTD. Based on a new intelligent inference technique, we 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 (i) distinguishes link problems from client problems and (ii) identifies characteristics unique to the specific fault to report the root cause. The modular design of the system enables support for new access link and fault types. Experimental evaluation demonstrated the capability of the IACD system to distinguish between faulty and healthy links and to diagnose the client faults with 98% accuracy. The system can perform fault diagnosis independent of the user's specific TCP implementation, enabling diagnosis of diverse range of client devices