Server virtualization by user behaviour model using a data mining technique — A preliminary study

Abstract

Server virtualization is the masking of server resources, including the number and identity of individual physical servers, processors, and operating systems, from server users. However, the problem of tuning dynamic resource allocation is a novelty. Managing heterogeneous workloads running within virtual machines is an interesting and challenging topic of server virtualization. This research applied association rule discovery, which is one of the data mining techniques to predict level of user access. The results illustrate that performance of the predictive model for a proxy server is 86.86%. The performance of the predictive model for a web server is 87.18%. Additionally, user behaviors for proxy and web servers are visualized. The results suggest that user behaviors are different in term of workload, day and time usage. This preliminary study may be an approach to improve management of data centers running heterogeneous workloads using server virtualization.