Software Aging Analysis of Web Server Using Neural Networks

TL;DR

This paper presents a neural network-based method to optimize software rejuvenation schedules for web servers, aiming to prevent performance degradation caused by software aging.

Contribution

It introduces an RBF neural network approach to accurately and efficiently determine rejuvenation schedules for web servers, improving upon existing methods.

Findings

RBF neural networks provide better accuracy in predicting aging indicators.

The proposed method converges faster than traditional approaches.

Optimized rejuvenation schedules reduce server downtime and improve reliability.

Abstract

Software aging is a phenomenon that refers to progressive performance degradation or transient failures or even crashes in long running software systems such as web servers. It mainly occurs due to the deterioration of operating system resource, fragmentation and numerical error accumulation. A primitive method to fight against software aging is software rejuvenation. Software rejuvenation is a proactive fault management technique aimed at cleaning up the system internal state to prevent the occurrence of more severe crash failures in the future. It involves occasionally stopping the running software, cleaning its internal state and restarting it. An optimized schedule for performing the software rejuvenation has to be derived in advance because a long running application could not be put down now and then as it may lead to waste of cost. This paper proposes a method to derive an accurate and optimized schedule for rejuvenation of a web server (Apache) by using Radial Basis Function (RBF) based Feed Forward Neural Network, a variant of Artificial Neural Networks (ANN). Aging indicators are obtained through experimental setup involving Apache web server and clients, which acts as input to the neural network model. This method is better than existing ones because usage of RBF leads to better accuracy and speed in convergence.