Using a recurrent artificial neural network for dynamic self-adaptation of cluster-based web-server systems

Abstract

To process huge requests issued from web users, web servers often set up a cluster using switches and gateways where a switch directs users' requests to some gateway. Each gateway, which is connected to some servers, is considered for processing a specific type of request such as fttp or http service. When servers of a gateway are saturated and the gateway is not able to process more requests, adaptation is performed by borrowing a server from another gateway. However, such a reactiveadaptation causes some problems. However, due to problem of the reactive techniques, predictive ones have been paid attention. While a reactive adaptation aims to redress the system after incurring a bottleneck, a predictiveadaptation tries to prevent the system from entering the bottleneck. In this article, we improved our previous predictive framework using a Recurrent Artificial Neural Network (RANN) called Nonlinear Autoregressive with eXogenous (external) inputs (NARX). We employed our new framework for adaptation of a web-based cluster where each cluster is meant for a specific service and self-adaptation is used for load balancing clusters. To show the improvement, we used the case study presented in our previous study.