Load prediction of virtual machine servers using genetic expression programming

Abstract

Virtualization is a key technology for cloud-computing, which creates various types of virtual computing resources on physical machines. A center of virtual machine (VM) servers manages different load situations of servers and adjusts flexibly the consumptions of physical resources to achieve better cost-performance efficiency. One of the key problems in the management of VM servers (VMSs) is load prediction with which decisions for load-balance as well as other management issues can be engaged. This study employs genetic expression programming (GEP) for deriving regression models of load of VMSs. GEP regression models are “white-boxes” that have visible structures and can be modified and integrated with other VM management mechanisms. Data representing the types of VM resources, VM loads, etc., are collected for training GEP models. With the GEP models, one can predict the work load of VMSs so that precise decisions of load-balance can be made. The experimental results show that GEP can generate precise models for load prediction of VMSs than other methods.