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

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.

A Hybrid Classifier Using Reduced Signatures for Automated Soft-Failure Diagnosis in Network End-User Devices

“…Our search on the research literature has revealed the lack of a fully automated solution for identifying the root causes of network soft-failures using TCP traces. To fill this gap, we previously proposed an automated diagnostic system based on supervised Machine Learning (ML) algorithms and network fault signatures 1 created using aggregated TCP statistics [8], [9]. In our system, the ML algorithms are first trained using signatures that we call as Normalized Statistical Signatures (NSS), which are generated from collected packet traces.…”

Automated diagnosis of known and unknown soft-failure in user devices using transformed Signatures and single classifier architecture

“…As a method to automate the diagnosis of soft-failures in UDs, authors have previously proposed diagnostic systems based on supervised Machine Learning (ML) techniques and fault signatures [8], [9]. The ML algorithms in these systems are first trained using signatures called "Normalized Statistical Signatures (NSS)" generated from packet traces of controlled Transmission Control Protocol (TCP) connections between diagnostic server and UDs with known faults.…”