Ensemble Method for System Failure Detection Using Large-Scale Telemetry Data

TL;DR

This paper introduces an ensemble machine learning approach combining LSTM, isolation forests, OCSVM, and LOF to detect system failures from large-scale telemetry data, improving system reliability.

Contribution

It presents a novel ensemble methodology integrating multiple algorithms for effective failure detection using extensive telemetry data.

Findings

High detection rate of system failures

Effective identification of failure patterns

Enhanced system reliability insights

Abstract

The growing reliance on computer systems, particularly personal computers (PCs), necessitates heightened reliability to uphold user satisfaction. This research paper presents an in-depth analysis of extensive system telemetry data, proposing an ensemble methodology for detecting system failures. Our approach entails scrutinizing various parameters of system metrics, encompassing CPU utilization, memory utilization, disk activity, CPU temperature, and pertinent system metadata such as system age, usage patterns, core count, and processor type. The proposed ensemble technique integrates a diverse set of algorithms, including Long Short-Term Memory (LSTM) networks, isolation forests, one-class support vector machines (OCSVM), and local outlier factors (LOF), to effectively discern system failures. Specifically, the LSTM network with other machine learning techniques is trained on Intel Computing Improvement Program (ICIP) telemetry software data to distinguish between normal and failed system patterns. Experimental evaluations demonstrate the remarkable efficacy of our models, achieving a notable detection rate in identifying system failures. Our research contributes to advancing the field of system reliability and offers practical insights for enhancing user experience in computing environments.