Joint study on VMs deployment, assignment and migration in geographically distributed data centers

Abstract

Enterprises build private clouds to provide IT resources for geographically distributed subsidiaries or product divisions. Public cloud providers like Amazon lease their platforms to enterprise users, thus, enterprises can also rent a number of virtual machines (VMs) from their data centers in the service provider networks. Unfortunately, the network cannot always guarantee stable connectivity for their clients to access the VMs or low-latency transfer among data centers. Usually, both latency and bandwidth are in unstable network environment. Being affected by background traffics, the network status can be volatile. To reduce the latency uncertainty of client accesses, enterprises should consider the network status when they deploy data centers or rent virtual data centers from cloud providers. In this paper, we first develop a data center deployment and assignment scheme for an enterprise to meet its users’ requirements under uncertain network status. To accommodate to the changes of the network status and users’ demands, a VMs migration-based redeployment scheme is adopted. These two schemes work in a joint way, and lay out a framework to help enterprises make better use of private or public clouds.

Keywords

data center deployment / VMs migration / Min-Max stochastic optimization

References

[1] He K, Fisher A,Wang L, Gember A, Akella A, Ristenpart T. Next stop, the cloud: understanding modern web service deployment in EC2 and Azure. In: Proceedings of the 2013 Conference on Internet Measurement Conference. 2013, 177–190

[2] Shue D, Freedman M J,Shaikh A. Fairness and isolation in multitenant storage as optimization decomposition. ACM SIGOPS Operating System Review, 2013, 47(1): 16–21

[3] Wu Z, Madhyastha H V. Understanding the latency benefits of multicloud webservice deployments. ACM SIGCOMM Computer Communication Review, 2013, 43(2): 13–20

[4] Zaharia M, Konwinski A, Joseph A D, Katz R, Stoica I. Improving MapReduce performance in heterogeneous environments. In: Proceedings of the 8th USENIX Conference on Operating Systems Design and Implementation. 2008, 29–42

[5] Wang G, Ng T S E. The impact of virtualization on network performance of amazon EC2 data center. In: Proceedings of IEEE Conference on Computer Communications. 2010, 1163–1171

[6] Bertsimas D, Doan X V, Natarajan K, Teo C P. Models for minimax stochastic linear optimization problems with risk aversion. Mathematics of Operations Research, 2010, 35(3): 580–602

[7] Kallitsis M G, Callaway R D, Devetsikiotis M, Michailidis G. Distributed and dynamic resource allocation for delay sensitive network services. In: Proceedings of IEEE Global Telecommunications Conference. 2008, 1432–1437

[8] Wood T, Ramakrishnan K K, Shenoy P, Van der Merwe J. Cloudnet: dynamic pooling of cloud resources by live WAN migration of virtual machines. In: Proceedings of the 7th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments. 2011, 121–132

[9] Goldberg A V, Tarjan R E. Finding minimum-cost circulations by canceling negative cycles. Journal of the ACM, 1989, 36(4): 873–886

[10] Löfberg J. YALMIP: a toolbox for modeling and optimization in MATLAB. In: Proceedings of IEEE International Symposium on Computer Aided Control Systems Design. 2004, 284–289

[11] Headquarters C. Data Center Networking: Enterprise Distributed Data Centers. 2003

[12] Louis Y. Distributed Virtual Data Center for Enterprise and Service Provider Cloud. 2012

[13] Hajjat M, Sun X, Sung Y W E, Maltz D, Rao S, Sripanidkulchai K, Tawarmalani M. Cloudward bound: planning for beneficial migration of enterprise applications to the cloud. ACM SIGCOMM Computer Communication Review, 2011, 41(4): 243–254

[14] Chang H, Kodialam M, Lakshman T V, Mukherjee S, Wang L. Building access oblivious storage cloud for enterprise. In: Proceedings of the 2nd USENIX conference on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services. 2012, 5

[15] Bobroff N, Kochut A, Beaty K. Dynamic placement of virtual machines for managing SLA violations. In: Proceedings of the 10th IFIP/IEEE International Symposium on Integrated Network Management. 2007, 119–128

[16] Breitgand D, Epstein A. SLA-aware placement of multi-virtual machine elastic services in compute clouds. In: Proceedings of 2011 IFIP/IEEE International Symposium on Integrated Network Management. 2011, 161–168

[17] Meng X, Pappas V, Zhang L. Improving the scalability of data center networks with traffic-aware virtual machine placement. In: Proceedings of IEEE Conference on Computer Communications. 2010, 1154–1162

[18] Al-Kiswany S, Subhraveti D, Sarkar P, Ripeanu M. VMFlock: virtual machine co-migration for the cloud. In: Proceedings of the 20th International Symposium on High Performance Distributed Computing. 2011, 159–170

[19] Akoush S, Sohan R, Rice A, Moore A W, Hopper A. Predicting the performance of virtual machine migration. In: Proceedings of IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems. 2010, 37–46

[20] Breitgand D, Kutiel G, Raz D. Cost-aware live migration of services in the cloud. In: Proceedings of the 3rd Annual Haifa Experimental Systems Conference. 2010

[21] Goudarzi H, Ghasemazar M, Pedram M. SLA-based optimization of power and migration cost in cloud computing. In: Proceedings of IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 2012, 172–179

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg