Journal of Computer Science and Technology

, Volume 34, Issue 1, pp 185–206 | Cite as

On Maximum Elastic Scheduling in Cloud-Based Data Center Networks for Virtual Machines with the Hose Model

  • Shuai-Bing Lu
  • Jie WuEmail author
  • Huan-Yang Zheng
  • Zhi-Yi Fang
Regular Paper


With the growing popularity of cloud-based data center networks (DCNs), task resource allocation has become more and more important to the efficient use of resource in DCNs. This paper considers provisioning the maximum admissible load (MAL) of virtual machines (VMs) in physical machines (PMs) with underlying tree-structured DCNs using the hose model for communication. The limitation of static load distribution is that it assigns tasks to nodes in a once-and-for-all manner, and thus requires a priori knowledge of program behavior. To avoid load redistribution during runtime when the load grows, we introduce maximum elasticity scheduling, which has the maximum growth potential subject to the node and link capacities. This paper aims to find the schedule with the maximum elasticity across nodes and links. We first propose a distributed linear solution based on message passing, and we discuss several properties and extensions of the model. Based on the assumptions and conclusions, we extend it to the multiple paths case with a fat tree DCN, and discuss the optimal solution for computing the MAL with both computation and communication constraints. After that, we present the provision scheme with the maximum elasticity for the VMs, which comes with provable optimality guarantee for a fixed flow scheduling strategy in a fat tree DCN. We conduct the evaluations on our testbed and present various simulation results by comparing the proposed maximum elastic scheduling schemes with other methods. Extensive simulations validate the effectiveness of the proposed policies, and the results are shown from different perspectives to provide solutions based on our research.


data center network (DCN) cloud distributed algorithm elasticity hose model optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

11390_2019_1890_MOESM1_ESM.pdf (344 kb)
ESM 1 (PDF 343 kb)


  1. [1]
    Bari M F, Boutaba R, Esteves R et al. Data center network virtualization: A survey. IEEE Communications Surveys and Tutorials, 2013, 15(2): 909-928.CrossRefGoogle Scholar
  2. [2]
    Mann Z A. Allocation of virtual machines in cloud data centers — A survey of problem models and optimization algorithms. ACM Computing Surveys, 2015, 48(1): Article No. 11.Google Scholar
  3. [3]
    Li K K, Wu J, Adam B. Elasticity-aware virtual machine placement for cloud datacenters. In Proc. the 2nd International Conference on Cloud Networking, November 2013, pp.99-107.Google Scholar
  4. [4]
    Duffield N G, Goyal P, Greenberg A et al. A flexible model for resource management in virtual private networks. In Proc. the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, August 1999, pp.95-108.Google Scholar
  5. [5]
    Lee Y T, Sidford A, Wong S C W. A faster cutting plane method and its implications for combinatorial and convex optimization. In Proc. the 56th Annual Symposium on Foundations of Computer Science, October 2015, pp.1049-1065.Google Scholar
  6. [6]
    Leiserson C E. Fat-trees: Universal networks for hardware efficient supercomputing. IEEE transactions on Computers, 1985, C-34(10): 892-901.CrossRefGoogle Scholar
  7. [7]
    Al-Fares M, Loukissas A, Vahdat A. A scalable, commodity data center network architecture. In Proc. the ACM SIGCOMM 2008 Conference on Data communication, August 2008, pp.63-74.Google Scholar
  8. [8]
    Davie B S, Rekhter Y. MPLS: Technology and Applications (1st edition). Morgan Kaufmann, 2000.Google Scholar
  9. [9]
    Liu Y, Muppala J K, Veeraraghavan M et al. Data Center Networks: Topologies, Architectures and Fault-Tolerance Characteristics. Springer International Publishing, 2013.Google Scholar
  10. [10]
    Wang R X, Wickboldt J A, Esteves R P et al. Using empirical estimates of effective bandwidth in network-aware placement of virtual machines in data centers. IEEE Transactions on Network and Service Management, 2016, 13(2): 267-280.CrossRefGoogle Scholar
  11. [11]
    Kusic D, Kephart J O, Hanson J E et al. Power and performance management of virtualized computing environments via look a head control. Cluster Computing, 2009, 12(1): 1-15.CrossRefGoogle Scholar
  12. [12]
    Xu F, Liu F, Liu L et al. iAware: Making live migration of virtual machines interference-aware in the cloud. IEEE Transactions on Computers, 2014, 63(12): 3012-3025.MathSciNetCrossRefzbMATHGoogle Scholar
  13. [13]
    Yang S, Wieder P, Yahyapour R et al. Reliable virtual machine placement and routing in clouds. IEEE Transactions on Parallel and Distributed Systems, 2017, 28(10): 2965-2978.CrossRefGoogle Scholar
  14. [14]
    Guo C X, Lu G H, Wang H J et al. SecondNet: A data center network virtualization architecture with bandwidth guarantees. In Proc. the 6th International Conference, November 2010, Article No. 15.Google Scholar
  15. [15]
    Deng W, Liu F, Jin H et al. Reliability-aware server consolidation for balancing energy-lifetime tradeoff in virtualized cloud datacenters. International Journal of Communication Systems, 2014, 27(4): 623-642.CrossRefGoogle Scholar
  16. [16]
    Liu F M, Guo J, Huang X et al. eBA: Efficient bandwidth guarantee under traffic variability in datacenters. IEEE/ACM Transactions on Networking, 2017, 25(1): 506-519.CrossRefGoogle Scholar
  17. [17]
    Li X, Wu J, Tang S et al. Let’s stay together: Towards traffic aware virtual machine placement in data centers. In Proc. the 33rd IEEE International Conference on Computer Communications, April 2014, pp.1842-1850.Google Scholar
  18. [18]
    Meng X Q, Pappas V, Zhang L. Improving the scalability of data center networks with traffic-aware virtual machine placement. In Proc. the 29th IEEE International Conference on Computer Communications, March 2010, pp.1154-1162.Google Scholar
  19. [19]
    Xu F, Liu F M, Jin H et al. Managing performance overhead of virtual machines in cloud computing: A survey, state of the art, and future directions. Proceedings of the IEEE, 2014, 102(1): 11-31.CrossRefGoogle Scholar
  20. [20]
    Kumar A, Rastogi R, Silberschatz A et al. Algorithms for provisioning virtual private networks in the hose model. IEEE/ACM Transactions on Networking, 2002, 10(4): 565-578.CrossRefGoogle Scholar
  21. [21]
    Ballani H, Costa P, Karagiannis T et al. Towards predictable datacenter networks. In Proc. the 2011 ACM Conference on SIGCOMM, August 2011, pp.242-253.Google Scholar
  22. [22]
    Lee J, Turner Y, Lee M et al. Application-driven bandwidth guarantees in datacenters. In Proc. the 2014 ACM Conference on SIGCOMM, August 2014, pp.467-478.Google Scholar
  23. [23]
    Erlebach T, Ruegg M. Optimal bandwidth reservation in hose-model VPNs with multi-path routing. In Proc. IEEE INFOCOM 2004, March 2004, pp.2275-2282.Google Scholar
  24. [24]
    Zhu J, Li D, Wu J P et al. Towards bandwidth guarantee in multi-tenancy cloud computing networks. In Proc. the 20th IEEE International Conference on Network Protocols, October 2012.Google Scholar
  25. [25]
    Dutta D, Kapralov M, Post I et al. Optimal bandwidth aware VM allocation for Infrastructure-as-a-Service. arXiv:1202.3683, 2012., Feb. 2018.
  26. [26]
    Plummer D C, Smith D M, Bittman T J et al. Five refining attributes of public and private cloud computing. Technical Report, Gartner, 2009., May 2018.
  27. [27]
    Lu S B, Fang Z Y, Wu J et al. Elastic scaling of virtual clusters in cloud data center networks. In Proc. the 36th International Performance Computing and Communications Conference, December 2017, pp.1-8.Google Scholar
  28. [28]
    Herbst N R, Kounev S, Reussner R. Elasticity in cloud computing: What it is, and what it is not. In Proc. the 10th International Conference on Autonomic Computing, June 2013, pp.23-27.Google Scholar
  29. [29]
    Shawky D M, Ali A. Defining a measure of cloud computing elasticity. In Proc. the 1st International Conference on Systems and Computer Science, August 2012, pp.1-5.Google Scholar
  30. [30]
    Li K K, Wu J, Blaisse A. Elasticity-aware virtual machine placement in k-ary cloud data centers. Parallel and Cloud Computing, 2014, 3(2): 22-31.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Shuai-Bing Lu
    • 1
    • 2
  • Jie Wu
    • 2
    Email author
  • Huan-Yang Zheng
    • 2
  • Zhi-Yi Fang
    • 1
  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Department of Computer and Information SciencesTemple UniversityPhiladelphiaUSA

Personalised recommendations