Increasing Server Availability for Overall System Security: A Preventive Maintenance Approach Based on Failure Prediction
This work addresses server availability issues for security-critical systems, but it is incremental as it applies a known method (ANN) to a specific domain without broad new insights.
The paper tackled the problem of server availability for system security by using an artificial neural network to predict software aging and resource exhaustion in Apache web servers, achieving results benchmarked against existing statistical and empirical models.
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.