Improved pre-copy algorithm using statistical prediction and compression model for efficient live memory migration

被引：1

作者：

Patel M. ^{[1
]}

Chaudhary S. ^{[2
]}

Garg S. ^{[3
]}

机构：

[1] Computer Engineering Department, A.D. Patel Institute of Technology, Anand, Gujarat

[2] Institute of Engineering and Technology (IET), Ahmedabad University, Ahmedabad, Gujarat

[3] Institute of Technology, Nirma University, Ahmedabad, Gujarat

来源：

International Journal of High Performance Computing and Networking | 2018年 / 11卷 / 01期

关键词：

ARIMA; Auto-regressive integrated moving average; Delta compression; Dirty pages; Downtime; Least recently used; LRU; Pre-copy; Prediction; Time-series data; Total migration time; Virtualisation;

D O I：

10.1504/IJHPCN.2018.10009625

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Xen hypervisor is used to execute and migrate the guests on different architectures using a pre-copy algorithm. There are three major categories to improve pre-copy using live migration algorithms: 1) reducing dirty pages; 2) predicating dirty pages; 3) compressing memory pages. The methods based on reducing dirty pages can lead to performance degradation so the new approach called combined approach (including prediction and compression) is proposed in this paper. The prediction of dirty pages during a migration is performed using autoregressive integrated moving average (ARIMA) model. A least recently used (LRU) stack distance-based delta compression algorithm is proposed for compression model to achieve efficient virtual machine migration. The results show that ARIMA-based model is able to predict 93% in the case of high dirty pages environment. The combined approach is able to reduce 19.16% downtime and 10.76% total migration time on an average compared to Xen's pre-copy algorithm. © Copyright 2018 Inderscience Enterprises Ltd.

引用

页码：55 / 65

页数：10

共 31 条

[1]

Ahmad R.W., Gani A., Hamid S.H.A., Shiraz M., Xia F., Madani S.A., Virtual machine migration in cloud data centers: A review, taxonomy, and open research issues, Center for Mobile Cloud Computing (C4MCC), the Journal of Supercomputing, 71, 7, pp. 2473-2515, (2015)

[2]

Alamdari J.F., Zamanifar K., A reuse distance based precopy approach to improve live migration of virtual machines, 2nd IEEE International Conference on Parallel Distributed and Grid Computing (PDGC), pp. 551-556, (2012)

[3]

Barham P., Dragovic B., Fraser K., Hand S., Harris T., Xen and the art of virtualization, Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, 37, 5, pp. 164-177, (2003)

[4]

Chen C., Cao J., Prediction-based optimization of live virtual machine migration, Network and Parallel Computing, 8707, pp. 347-356, (2014)

[5]

Chen Y.-L., Yang Y.-C., Lee W.-T., The study of using game theory for live migration prediction over cloud computing, Intelligent Data Analysis and Its Applications, 2, pp. 417-425, (2014)

[6]

Clark C., Fraser K., Hand S., Hanseny J.G., July E., Limpach C., Pratt I., Warfield A., Live migration of virtual machines, Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation, 2, pp. 273-286, (2005)

[7]

Cui W., Song M., Live memory migration with matrix bitmap algorithm, IEEE 2nd Symposium on Web Society, pp. 16-17, (2010)

[8]

Cui Y., Lin Y., Guo Y., Li R., Wang Z., Optimizing live migration of virtual machines with context based prediction algorithm, International Workshop on Cloud Computing and Information Security (CCIS), (2013)

[9]

Fortis T.-F., Munteanu V.I., Negru V., A taxonomic view of cloud computing services, International Journal of Computational Science and Engineering, 11, 1, pp. 17-28, (2015)

[10]

Hsu C.-H., Peng S.-J., Chan T.-Y., Slagter K., Chung Y.-C., An adaptive pre-copy strategy for virtual machine live migration, Internet of Vehicles Technologies and Services, 8662, 39, pp. 396-406, (2014)

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