A kind of strategy for the deployment and scheduling of virtual machines is proposed based on multiple attributes analysis to solve the uneven loads problem among physical servers in the cloud computing. The strategy classifies virtual machines according to the characteristic of resources
and it is composed of two aspects. Resources of hot spots are analyzed to quantify their important degrees in the virtual machines deployment
and evaluate all the physical servers according to the virtual machine vector
then the best physical server is selected to deploy. The vectors of virtual machines which are running on the overload physical servers are obtained in the virtual machines scheduling
and the rest of physical servers are evaluated in the right order. This strategy not only realizes the optimal allocation of the resources and reduces the overall loss caused by dynamic load balancing. The experimental results show that
when 20 virtual machines in five physical machines are applied in the same order
the average number of dynamic migration of the proposed algorithm significantly reduces about 80% than that the random equalization strategy does
and the rates of physical server resources usage are more balanced.
关键词
Keywords
references
BUYYA R, YEO C S, VENUGOPAL S. Market-oriented cloudcomputing: vision, hype, and reality for delivering it services as computing utilities [C]∥Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications. Piscataway, NJ, USA: IEEE Computer Society, 2008: 5-13.
ARMBRUST M, FOX A, GRIFFITH R, et al, Above the clouds: a Berkeley view of cloud computing, UCB/EECS-2009-28 [R]. Springfield, USA: University of California, Berkeley. Electrical Engineering and Computer Sciences Department. 2009.
BARHAM P, DRAGOVIC B, FRASER K, et al. Xen and the art of virtualization [C]∥Proceedings of the 19th ACM Symposium on Operating Systems Principles. New York, USA: ACM, 2003: 164-177.
VMWare Inc. Resource management with VMWare DRS [EB/OL].(2006-06-05)[2012-04-10].http:∥www.Vmware.com/vmtn/resources/401.
ZHUANG Wei, GUI Xiaolin, HUANG Ruwei, et al. TCP DDOS attack detection on the host in the KVM virtual machine environment [C]∥Proceedings of the 11th IEEE/ACIS International Conference on Computer and Information Science. Piscataway, NJ, USA: IEEE Computer Society, 2012: 62-67.
MATTHEWS J N, DOW E M, DESHANE T, et al. Running Xen: a hands-on guide to the art of virtualization [M]. Boston, USA: Prentice Hall, 2008.
CLARK C, FRASER K, HAND S, et al. Live migration of virtual machines [C]∥Proceedings of the Second Conference on Symposium on Networked Systems Design and Implementation. New York, USA: ACM, 2005:273-286.
HU Liting, JIN Hai, LIAO Xiaofei, et al. Magnet: a novel scheduling policy for power reduction in cluster with virtual machines [C]∥Proceedings of the 2008 IEEE International Conference on Cluster Computing. Piscataway, NJ, USA: IEEE Computer Society, 2008: 13-22.
ZHOU Wenyu, CHEN Huaping, YANG Shoubao, et al. Resource scheduling in virtual machine cluster based on live migration of virtual machine [J]. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2011, 39(S1): 130-133.
FADWA G, MEDIA A. Web based multi criteria decision making using AHP method [C]∥Proceedings of the 2010 International Conference on Information and Communication Technology for the Muslim World. Piscataway, NJ, USA: IEEE Computer Society, 2010: 6-12.
CAO Jian, YE Feng, ZHOU Gengui, et al. A new method for VE partner selection and evaluation based on AHP and Fuzzy theory [C]∥Proceedings of The 8th International Conference on Computer Supported Cooperative Work in Design. Ontario, Canada: NRC Research Press, 2004: 563-566.