Abstract
Due to having many advantages, virtualization technology has been widely used and become a key technique of cloud computing. Live migration of virtual machines is the core and key technique of virtualization fields, but the existing pre-copy live migration approach has the problems of low copy efficiency and long total migration time, so we propose an extreme learning machine (ELM) based adaptive live migration approach of virtual machines (ELMBALMA) in this chapter. Firstly, the approach uses the ELM algorithm to classify the virtual machines according to the type of the running applications, and then choose the best suitable migration algorithms for each type of virtual machines, thereby reduce the time of live migrating of virtual machines. In addition, we proposed a memory compression based live migration algorithm (MCBLMA) for the memory-intensive application scene. The algorithm uses a weight-based measurement method of writable working set, which can accurately measure the writable working set, so that it can reduce the amount of dirty memory page transmission, meanwhile it uses a memory compression algorithm to compress memory pages to be transmitted, and thus reduces the data transmission time. Preliminary experiments show that the proposed approach can significantly reduce the memory pages transmitted, the total migration time and the downtime of virtual machines.
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Acknowledgments
This research was supported by the National Natural Science Foundation of China (No. 61073063, 61173029, 61272182 and 61173030), the Ocean Public Welfare Scientific Research Project of State Oceanic Administration of China (No. 201105033), and National Digital Ocean Key Laboratory Open Fund Projects (No. KLDO201306).
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Qiao, B. et al. (2014). ELM-Based Adaptive Live Migration Approach of Virtual Machines. In: Sun, F., Toh, KA., Romay, M., Mao, K. (eds) Extreme Learning Machines 2013: Algorithms and Applications. Adaptation, Learning, and Optimization, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-04741-6_9
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DOI: https://doi.org/10.1007/978-3-319-04741-6_9
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