期刊文献+

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

Joint study on VMs deployment, assignment and migration in geographically distributed data centers
原文传递
导出
摘要 Enterprises build private clouds to provide IT re- sources for geographically distributed subsidiaries or prod- uct 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 net- work 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 cen- ters from cloud providers. In this paper, we first develop a data center deployment and assignment scheme for an enter- prise 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. Enterprises build private clouds to provide IT re- sources for geographically distributed subsidiaries or prod- uct 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 net- work 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 cen- ters from cloud providers. In this paper, we first develop a data center deployment and assignment scheme for an enter- prise 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.
出处 《Frontiers of Computer Science》 SCIE EI CSCD 2016年第3期559-573,共15页 中国计算机科学前沿(英文版)
基金 This work was supported in part by the National Basic Research Program of China (2010CB328105, 2009CB320504), the National Natural Science Foundation of China (NSFC) (Grant No. 60932003). We would like to thank the anonymous reviewers for their suggestions that help us improve this paper.
关键词 data center deployment VMs migration Min-Max stochastic optimization data center deployment, VMs migration, Min-Max stochastic optimization
  • 相关文献

参考文献21

  • 1He 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 Measure- ment Conference. 2013, 177-190.
  • 2Shue D, Freedman M J, Shaikh A. Fairness and isolation in multi- tenant storage as optimization decomposition. ACM SIGOPS Operat- ing System Review, 2013, 47(1): 16-21.
  • 3Wu Z, Madhyastha H V. Understanding the latency benefits of multi- cloud webservice deployments. ACM SIGCOMM Computer Commu- nication Review, 2013, 43(2): 13-20.
  • 4Zaharia M, Konwinski A, Joseph A D, Katz R, Stoica I. Improving MapReduce performance in heterogeneous environments. In: Proceed- ings of the 8th USENIX Conference on Operating Systems Design and Implementation. 2008, 29-42.
  • 5Wang G, Ng T S E. The impact of virtualization on network perfor- mance of amazon EC2 data center. In: Proceedings of IEEE Confer- ence on Computer Communications. 2010, 1163-1171.
  • 6Bertsimas D, Doan X V, Natarajan K, Teo C P. Models for minimax stochastic linear optimization problems with risk aversion. Mathemat- ics of Operations Research, 2010, 35(3): 5812--602.
  • 7Kallitsis M G, Callaway R D, Devetsikiotis M, Michailidis G. Dis- tributed and dynamic resource allocation for delay sensitive network services. In: Proceedings of IEEE Global ference. 2008, 1432-1437.
  • 8Wood 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 Inter- national Conference on Virtual Execution Environments. 2011, 121- 132.
  • 9Goldberg A V, Tarjan R E. Finding minimum-cost circulations by can- celing negative cycles. Journal of the ACM, 1989, 36(4): 873-886.
  • 10LOfberg J. YALMIP: a toolbox for modeling and optimization in MAT- LAB. In: Proceedings of IEEE International Symposium on Computer Aided Control Systems Design. 2004, 284-289.

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部