Advertisement

Mobile Networks and Applications

, Volume 22, Issue 2, pp 161–173 | Cite as

UFalloc: Towards Utility Max-min Fairness of Bandwidth Allocation for Applications in Datacenter Networks

Article

Abstract

Providing fair bandwidth allocation for applications is becoming increasingly compelling in cloud datacenters, as different applications compete for the shared datacenter network resources. While existing solutions mainly provide bandwidth guarantees for virtual machines (VMs) or tenants with the aim of achieving the VM-level or tenant-level fairness of bandwidth allocation, scant attention has been paid to providing bandwidth guarantees for applications to achieve the fairness of application performance (utility). In this paper, we introduce a rigorous definition of application-level utility max-min fairness, which guides us to develop a non-linear model to investigate the relationship between the utility fairness and bandwidth allocation for applications. Based on such a model, we further arbitrate the intrinsic tradeoff between the network bandwidth utilization and utility fairness of application bandwidth allocation, using a tunable fairness relaxation factor. To improve the bandwidth utilization while maintaining the strict utility fairness of bandwidth allocation, we design UFalloc, an application-level Utility max-min Fair bandwidth allocation strategy in datacenter networks. With extensive experiments using OpenFlow in Mininet virtual network environment, we demonstrate that UFalloc can achieve high utilization of network bandwidth while maintaining the utility max-min fair share of bandwidth allocation with a certain degree of fairness relaxation, yet with an acceptable computational overhead.

Keywords

Bandwidth allocation Max-min fairness Bandwidth utilization Application utility Tradeoff Datacenter networking 

References

  1. 1.
    Roy A, Zeng H, Bagga J, Porter G, Snoeren AC (2015) Inside the social network’s (datacenter) network. In: Proceedings of SIGCOMM, pp 123–137Google Scholar
  2. 2.
    Yi X, Liu F, Liu J, Jin H (2014) Building a network highway for big data: architecture and challenges. IEEE Network Magazine 28(4):5–13CrossRefGoogle Scholar
  3. 3.
    Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113CrossRefGoogle Scholar
  4. 4.
    Wang H, Chen L, Chen K, Li Z, Zhang Y, Guan H, Qi Z, Li D, Geng Y (2015) Flowprophet: generic and accurate traffic prediction for data-parallel cluster computing. In: Prof. of ICDCS, pp 349–358Google Scholar
  5. 5.
    Xu F, Liu F, Jin H, Vasilakos AV (2014) Managing performance overhead of virtual machines in cloud computing: a survey, state of art and future directions. Proc IEEE 102(1):11–31CrossRefGoogle Scholar
  6. 6.
    Bertsekas DP, Gallager RG (1992) Data network, 2nd edn. Prentice-Hall, LondonMATHGoogle Scholar
  7. 7.
    Kelly FP, Maulloo AK, Tan DKH (1998) Rate control for communication networks: shadow price, proportional fairness and stability. J Oper Res Soc 49(3):237–252CrossRefMATHGoogle Scholar
  8. 8.
    Guo J, Liu F, Tang H, Lian Y, Jin H, Lui J (2013) Falloc: fair network bandwidth allocation in IaaS datacenters via a bargaining game approach. In: Proceedings of ICNP, pp 1–10Google Scholar
  9. 9.
    Yu L, Cai Z (2016) Dynamic scaling of virtual clusters with bandwidth guarantee in cloud data centers. In: Proceedings of infocomGoogle Scholar
  10. 10.
    Popa L, Kumar G, Chowdhury M, Krishnamurthy A, Ratnasamy S, Stoica I (2012) Faircloud: Sharing the Network in Cloud Computing. In: Proceedings of SIGCOMM, pp 187–198Google Scholar
  11. 11.
    Lam T, Radhakrishnan S, Vahdat A, Varghese G (2010) Netshare: virtualizing data center networks across services. Technical Report CS2010-0957, Department of Computer Science and Engineering, University of California, San DiegoGoogle Scholar
  12. 12.
    Xie D, Ding N, Hu YC, Kompella R (2012) The only constant is change: incorporating time-varying network reservations in data centers. ACM SIGCOMM Computer Communication Review 42(4):199–210CrossRefGoogle Scholar
  13. 13.
    Wang XH, Han DF, Sun FY (1990) Point estimates on deformation newton’s iterations. Mathematica Numerica Sinica 1(2):145–156MathSciNetMATHGoogle Scholar
  14. 14.
    Ye W, Xu F, Zhang W (2015) Achieving application-level utility max-min fairness of bandwidth allocation in datacenter networks. In: Proceedings of collaboratecomGoogle Scholar
  15. 15.
    Jalaparti V, Bodik P, Menache I, Rao S, Makarychev K, Caesar M (2015) Network-aware scheduling for data-parallel jobs: plan when you can. In: Proceedings of SIGCOMM, pp 407–420Google Scholar
  16. 16.
    Shenker S (1995) Fundamental design issues for the future internet. IEEE J Sel Areas Commun 13(7):1176–1187CrossRefGoogle Scholar
  17. 17.
    Cao Z, Zegura E (1999) Utility max-min: an application-oriented allocation scheme. In: Proceedings of infocom, pp 793–801Google Scholar
  18. 18.
    Al-Fares M, Loukissas A, Vahdat A (2008) A scalable, commodity data center network architecture. ACM SIGCOMM Computer Communication Review 38(4):63–74CrossRefGoogle Scholar
  19. 19.
    Mo J, Walrand J (2000) Fair end-to-end window-based congestion control. IEEE/ACM Trans Networking 8(5):556–567CrossRefGoogle Scholar
  20. 20.
    Kuhn HW (2014) Nonlinear programming: a historical view. Traces and Emergence of Nonlinear Programming:393– 414Google Scholar
  21. 21.
    Slater M (2014) Lagrange multipliers revisited. Springer, BaselCrossRefGoogle Scholar
  22. 22.
    Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University PressGoogle Scholar
  23. 23.
    Boţ R, Kassay G, Wanka G (2005) Strong duality for generalized convex optimization problems. J Optim Theory Appl 127(1):45–70MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Li D (1995) Zero duality gap for a class of nonconvex optimization problems. J Optim Theory Appl 85 (2):309–324MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Rosen JB (1960) The gradient projection method for nonlinear programming. Part i. Linear constraints. J Soc Ind Appl Math 8(1):181–217CrossRefMATHGoogle Scholar
  26. 26.
    Yaïche H, Mazumdar RR, Rosenberg C (2000) A game theoretic framework for bandwidth allocation and pricing in broadband networks. IEEE/ACM Trans Networking 8(5):667–678CrossRefGoogle Scholar
  27. 27.
    Sohrab HH (2003) Basic real analysis. Switzerland, BirkhauserCrossRefMATHGoogle Scholar
  28. 28.
    Open Networking Foundation: OpenFlow Switch Specification Version 1.3.0 (Wire Protocol 0x04). https://www.opennetworking.org/images/stories/downloads/sdn-resources/onf-specifications/openflow/openflow-spec-v1.3.0.pdf (2012) [Online; released June 25, 2012]
  29. 29.
    Kelly F (2003) Fairness and stability of end-to-end congestion control*. Eur J Control 9(2):159–176CrossRefMATHGoogle Scholar
  30. 30.
    Wang W, Palaniswami M, Low S (2006) Application-Oriented Flow control fundamentals algorithms and fairness. IEEE/ACM Trans Networking 14(6):1282–1291CrossRefGoogle Scholar
  31. 31.
    Jin J, Wang W, Palaniswami M (2007) Utility Max-Min fair flow control for multipath communication networks. In: Proceedings of ICSPCS, pp 61–66Google Scholar
  32. 32.
    Xu F, Liu F, Liu L, Jin H, Li B, Li B (2014) iAware: making live migration of virtual machines interference-aware in the cloud. IEEE Trans Comput 63(12):3012–3025MathSciNetCrossRefGoogle Scholar
  33. 33.
    Xu F, Liu F, Jin H (2015) Heterogeneity and interference-aware virtual machine provisioning for predictable performance in the cloud. IEEE Trans ComputGoogle Scholar
  34. 34.
    Ballani H, Costa P, Karagiannis T, Rowstron A (2011) Towards predictable datacenter networks. ACM SIGCOMM Computer Communication Review 41(4):242–253CrossRefGoogle Scholar
  35. 35.
    Hu S, Bai W, Chen K, Tian C, Zhang Y, Wu H (2016) Providing bandwidth guarantees, work conservation and low latency simultaneously in the cloud. In: Proceedings of infocomGoogle Scholar
  36. 36.
    Shieh A, Kandula S, Greenberg A, Kim C, Saha B (2011) Sharing the data center network. In: Proceedings of NSDI, pp 309–322Google Scholar
  37. 37.
    Guo J, Liu F, Huang X, Lui J, Hu M, Gao Q, Jin H (2014) On efficient bandwidth allocation for traffic variability in datacenters. In: Proceedings of infocom, pp 1572–1580Google Scholar
  38. 38.
    Li D, Chen C, Guan J, Zhang Y, Zhu J, Yu R (2015) Dcloud: deadline-aware resource allocation for cloud computing jobs. IEEE Trans Parallel Distrib SystGoogle Scholar
  39. 39.
    Li D, Liao X, Jin H, Zhou B, Zhang Q (2013) A new disk I/O model of virtualized cloud environment. IEEE Trans Parallel Distrib Syst 24(6):1129–1138CrossRefGoogle Scholar
  40. 40.
    Kumar G, Chowdhury M, Ratnasamy S, Stoica I (2012) A case for performance-centric network allocation. In: Proceedings of hotcloud, pp 9–9Google Scholar
  41. 41.
    Lee J, Turner Y, Lee M, Popa L, Banerjee S, Kang JM, Sharma P (2014) Application-driven bandwidth guarantees in datacenters. In: Proceedings of SIGCOMM, pp 467–478Google Scholar
  42. 42.
    Chen L, Feng Y, Li B, Li B (2014) Towards performance-centric fairness in datacenter networks. In: Proceedings of infocom, pp 1599–1607Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina

Personalised recommendations