caa a22 4500 605477973 CHVBK 20210128100403.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10994-014-5448-x doi (NATIONALLICENCE)springer-10.1007/s10994-014-5448-x Selective switching mechanism in virtual machines via support vector machines and transfer learning [Elektronische Daten] [Wei Kuang, Laura Brown, Zhenlin Wang] 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. The Author(s), 2014 Memory virtualization nationallicence Support vector machines nationallicence Transfer learning nationallicence Kuang Wei Department of Computer Science, Michigan Technological University, Houghton, MI, USA aut Brown Laura Department of Computer Science, Michigan Technological University, Houghton, MI, USA aut Wang Zhenlin Department of Computer Science, Michigan Technological University, Houghton, MI, USA aut Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 137-161 0885-6125 101:1-3<137 2015 101 10994 https://doi.org/10.1007/s10994-014-5448-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-014-5448-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kuang Wei Department of Computer Science, Michigan Technological University, Houghton, MI, USA aut NATIONALLICENCE 700 1- Brown Laura Department of Computer Science, Michigan Technological University, Houghton, MI, USA aut NATIONALLICENCE 700 1- Wang Zhenlin Department of Computer Science, Michigan Technological University, Houghton, MI, USA aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 137-161 0885-6125 101:1-3<137 2015 101 10994