Virtual machine auto-configuration for web application

With the booming trend of cloud computing, on-demand resource management is overwhelming static and dedicated strategy. The increasing demands introduce multiple challenges including energy efficiency, performance enhancement, and fault tolerance. Virtualized computing environment decouples OS and applications with hardware to best facilitate these on-demand cloud services. In this paper, we propose an online learning approach for resource auto-configuration of distributed virtual machines to support multilayer web applications. Based on performance metrics from host OS, virtual machine, and application server, the approach is able to adjust resource configuration and direct virtual machine migration corresponding to service demand variations. Support vector regression is applied to control reconfiguration and migration. The approach will be evaluated by using TPC-E benchmark on multi-layer web applications deployed on networked virtual machines. Our approach will guide systems with proactive changes to improve dependability, efficiency, and reduce the power consumption.