Efficient Utilization of Mirroring Servers Using Artificial Neural Networks

Abstract

Due to the fact that an enormous amount of data is available to any user on the Internet, this leads to increased requests of Internet users to download or process their data among different servers. In fact, the need for increasing the reliability and performance of such servers has become an importance subject to tackle. Replicating servers is a way of reducing the overhead of Internet users’ requests as well as increasing the reliability and performance of servers. However, there is still a need to redirect users’ requests to a single server from those replications so these servers get unknown by Internet users. This technique is called mirroring servers. In this study, a new model that uses the Artificial Neural Networks (ANN) is proposed to select the appropriate server for any new user’s requests. In particular, this method considers the current features of servers with the new user’s requests as an input and provides the selected server as an output. The effectiveness of the proposed method is compared with two different techniques: Human’s selection after eliminating the required time from a user to make the selection and Round Robin selection. This comparison shows that the proposed method takes the advantages of these two techniques, which are based on the speed of selection for the Round Robin selection method and the selection of the best server according to the mirroring server features that are derived from the manual selection method. The results of this study indicate that the proposed model can improve the use of mirroring servers by 10% better than the Round Robin selection method since in this selection method, most of servers are idle in more than 25% of the time and do not have any more requests to serve.