Selective switching mechanism in virtual machines via support vector machines and transfer learning

Abstract

Virtualization is an essential technology in data centers allowing for a single machine to be used for multiple applications or users. With memory virtualization, two approaches, shadow paging (SP) and hardware-assisted paging (HAP), are taken by modern virtual machine memory managers. Neither memory mode is always preferred; previous studies have proposed to exploit the advantages of both modes by dynamically switching between these two paging modes based on the on-the-fly system behavior. However, the existing scheme makes the switching decision based on manual rules summarized for a specific architecture. This paper employs a machine learning approach that learns a decision model automatically and thus can adapt to different systems. Experimental results show that the performance of our switching mechanism can match or outperform either SP or HAP alone. Also, the results demonstrate that a machine learning-based decision model can match the performance of the hand-tuned model. Moreover, we further show that different hardware/software settings can affect on-the-fly system behavior and thus demand different decision models. Our scheme yields two effective decision models on two different machines. Additionally, transfer learning was used in order to efficiently train a model when faced with a new hardware configuration with only a limited number of training samples from the new machine.