Abstract
The resource utilization (CPU, memory) is a key performance metric in data center networks. The goal of the cloud platform supported by data center networks is achieving high average resource utilization while guaranteeing the quality of cloud services. Previous work focus on increasing the time-average resource utilization and decreasing the overload ratio of servers by designing various efficient virtual machine placement schemes. Unfortunately, most of virtual machine placement schemes did not involve the service level agreements and statistical methods. In this paper, we propose a correlation-aware virtual machine placement scheme that effectively places virtual machines on physical machines. First, we employ Neural Networks model to forecast the resource utilization trend according to the historical resource utilization data. Second, we design correlation-aware placement algorithms to enhance resource utilization while meeting the user-defined service level agreements. The results show that the efficiency of our virtual machine placement algorithms outperform the previous work by about 15%.
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Acknowledgement
This work has been supported in part by the China 973 Project (2014CB340303), China NSF Projects (Nos. 61672353, 61472252, 61672349 and 61303202), the Opening Project of Key Lab of Information Network Security of Ministry of Public Security (the Third Research Institute of Ministry of Public Security) (Grant no. C15602). The authors also would like to thank Wei Wei and Bo Cao for their contributions on the early versions of this paper.
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Chen, T., Zhu, Y., Gao, X., Kong, L., Chen, G., Wang, Y. (2018). Correlation-Aware Virtual Machine Placement in Data Center Networks. In: Wan, J., et al. Cloud Computing, Security, Privacy in New Computing Environments. CloudComp SPNCE 2016 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 197. Springer, Cham. https://doi.org/10.1007/978-3-319-69605-8_3
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DOI: https://doi.org/10.1007/978-3-319-69605-8_3
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