Assessment of Performance in Data Center Network Based on Maximum Flow

  • Kai Peng
  • Rongheng Lin
  • Binbin Huang
  • Hua Zou
  • Fangchun Yang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 260)


Recently, data center networks (DCN) have received significant attention from the academic and industry. However, researches of DCN are mainly concentrated on the improvement of network architectures and the design of routing protocols, or the performance evaluation from the perspective of node importance. In contrast to existing solutions, in this paper, we propose using maximum-flow theory to assess the network performance. Firstly, we abstract two kinds of typical DCN architectures and then formulate and convert the performance analysis of those architectures into a maximum-flow problem including a supersource and a supersink. Secondly, we get the value of maximum-flow by using Edmonds and Goldberg algorithm. Last but not the least, based on the theory of maximum-flow and Minimal cut sets, we get the critical edges for each architecture. Extended experiments and analysis show that our method is effective and indeed introduce low overhead on computation. In addition, the method and issues observed in this paper is generic and can be widely used in newly proposed DCN architectures.


DCN Topologies Maximum-flow Assessment 



This work is supported by the National Natural Science Fund China under Grant No. 2009CB320406, the National 863 High-tech Project of China under Grant No. 2011AA01A102, Funds for Creative Research Groups of China (60821001) and State Key Lab of Networking and Switching Technology. Ph.D. Programs Foundation of Ministry of Education (20110005130001).


  1. 1.
    Vaquero LM, Rodero-Merino L, Caceres J, Lindner M (2008) A break in the clouds: towards a cloud definition. ACM SIGCOMM Comput Commun Rev 39:50–55CrossRefGoogle Scholar
  2. 2.
    Peng K, Zou H, Lin R, Yang F (2012) Small business-oriented index construction of cloud data. In: Proceedings in 12th international conference on algorithms and architectures for parallel processing, pp 156–165Google Scholar
  3. 3.
    Guo C, Wu H, Tan K, Shi L, Zhang Y, Lu S (2008) Dcell: a scalable and fault-tolerant network structure for data centers. ACM SIGCOMM Comput Commun Rev 75–86Google Scholar
  4. 4.
    Li D, Guo C, Wu H, Tan K, Zhang Y, Lu S (2009) FiConn: using backup port for server interconnection in data centers. In: Proceedings in 28th conference on computer communications, pp 2276–2285Google Scholar
  5. 5.
    Guo C, Lu G, Li D, Wu H, Zhang X, Shi Y, Tian C, Zhang Y, Lu S (2009) BCube: a high performance, server-centric network architecture for modular data centers. ACM SIGCOMM Comput Commun Rev 39:63–74CrossRefGoogle Scholar
  6. 6.
    Leiserson CE (1985) Fat-trees: universal networks for hardware-efficient supercomputing. Comput IEEE Trans 100(10):892–901CrossRefGoogle Scholar
  7. 7.
    Greenberg A, Hamilton JR, Jain N, Kandula S, Kim C, Lahiri P, Maltz DA, Patel P, Sengupta S (2009) VL2: a scalable and flexible data center network. In: Proceedings of ACM SIGCOMM computer communication review, pp 51–62Google Scholar
  8. 8.
    Liao Y, Yin D, Gao L (2010) Dpillar: scalable dual-port server interconnection for data center networks. In: Proceedings in 19th international conference on computer communications and networks, pp 1–6Google Scholar
  9. 9.
    Shangguang W, Zheng Z, Qibo S, Hua Z, Fangchun Y (2011) Cloud model for service selection. In: Proceedings in 30th IEEE conference on computer communications workshops on cloud computing computer communications workshops, pp 666–671Google Scholar
  10. 10.
    Zhang Y, Su AJ, Jiang G (2011) Understanding data center network architectures in virtualized environments: a view from multi-tier applications. Comput Netw 55:2196–2208Google Scholar
  11. 11.
    Meng X, Pappas V, Zhang L (2010) Improving the scalability of data center networks with traffic-aware virtual machine placement. In: Proceedings in 29th IEEE conference on computer communications, pp 1–9Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Kai Peng
    • 1
  • Rongheng Lin
    • 1
  • Binbin Huang
    • 1
  • Hua Zou
    • 1
  • Fangchun Yang
    • 1
  1. 1.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations