Increasing Server Availability for Overall System Security: A Preventive Maintenance Approach Based on Failure Prediction

TL;DR

This paper proposes a preventive maintenance approach using Artificial Neural Networks to predict software aging in web servers, aiming to increase server availability and enhance overall system security.

Contribution

It introduces a novel application of ANN for predicting server resource exhaustion, improving server availability management compared to existing methods.

Findings

ANN outperforms traditional statistical models in prediction accuracy

Improved server availability through proactive maintenance scheduling

Benchmarking shows superior performance of ANN-based predictions

Abstract

Server Availability (SA) is an important measure of overall systems security. Important security systems rely on the availability of their hosting servers to deliver critical security services. Many of these servers offer management interface through web mainly using an Apache server. This paper investigates the increase of Server Availability by the use of Artificial Neural Networks (ANN) to predict software aging phenomenon. Several resource usage data is collected and analyzed on a typical long-running software system (a web server). A Multi-Layer Perceptron feed forward Artificial Neural Network was trained on an Apache web server data-set to predict future server resource exhaustion through uni-variate time series forecasting. The results were benchmarked against those obtained from non-parametric statistical techniques, parametric time series models and empirical modeling techniques reported in the literature.