Annals of Telecommunications

, Volume 73, Issue 3–4, pp 219–237 | Cite as

Constrained max-min fair scheduling of variable-length packet-flows to multiple servers

  • J. Khamse-Ashari
  • G. Kesidis
  • I. Lambadaris
  • B. Urgaonkar
  • Y. Zhao
Article
  • 61 Downloads

Abstract

In this paper, we study a multi-server queuing system wherein each user is constrained to get service only from a specified subset of servers. Fair packet scheduling in such a setting poses novel challenges that we address in this paper. Specifically, we observe that max-min fair allocation of the available resource over different servers (notably bandwidth) in the presence of placement constraints results in different levels of fair service-rates. To achieve the max-min fair service rates, we propose a novel packet scheduler which is inspired by the deficit-round robin (DRR) algorithm. The scheduler allocates tokens to flows in a round-by-round manner, where token allocation to flows at the beginning of each round is weighted max-min fair. So, we have called it multi-server max-min fair DRR (MSMF-DRR). The performance of the MSMF-DRR algorithm in terms of achieving fairness is shown through a worst-case performance analysis. In addition to analytical results, numerical experiments are also carried out to illustrate service isolation and the delay guarantee that are provided by the algorithm. Generally, a scheduler for such a constrained multi-server queuing system can be applicable in many modern data-networking applications, especially in cloud computing wherein virtual machines and/or processes vie for different IT resources distributed over heterogenous servers, while different processes may have preferences over servers owing to their quality-of-service requirements and the heterogeneity of servers.

Keywords

Packet scheduling K-server algorithms Placement constraints Max-min fairness Cloud computing Resource allocation Convex optimization 

References

  1. 1.
    Bennett JCR, Zhang H (1996) Wf2q: worst-case fair weighted fair queueing. In: Proceedings IEEE INFOCOM ’96, vol 1, pp 120–128Google Scholar
  2. 2.
    Bertsekas D, Gallager R (1992) Data networks. Prentice HallGoogle Scholar
  3. 3.
    Blanquer J, Ozden B (2001) Fair queuing for aggregated multiple links. In: Proceedings ACM SIGCOMMGoogle Scholar
  4. 4.
    Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University PressGoogle Scholar
  5. 5.
    Chandra A, Adler M, Goyal P, Shenoy P (2000) Surplus fair scheduling: a proportional-share cpu scheduling algorithm for symmetric multiprocessors. In: Proceedings USENIX conference on operating system design & implementationGoogle Scholar
  6. 6.
    Chandra A, Adler M, Shenoy P (2001) Deadline fair scheduling: bridging the theory and practice of proportionate fair scheduling in multiprocessor systems. In: Proceedings IEEE real-time technology and applications symposiumGoogle Scholar
  7. 7.
    Demers A, Keshav S, Shenker S (1989) Analysis and simulation of a fair queueing algorithm. In: ACM SIGCOMM Comput. Commun. Rev., vol 19Google Scholar
  8. 8.
    Floyd S, Jacobson V (1995) Link-sharing and resource management models for packet networks. IEEE/ACM Trans Networking 3(4):365–386CrossRefGoogle Scholar
  9. 9.
    Ghodsi A, Zaharia M, Shenker S, Stoica I (2013) Choosy: max-min fair sharing for datacenter jobs with constraints. In: Proceedings ACM EuroSys, pp 365–378Google Scholar
  10. 10.
    Golestani SJ (1994) A self-clocked fair queueing scheme for broadband applications. In: Proceedings IEEE INFOCOM 94, pp 636–646Google Scholar
  11. 11.
    Goyal P, Vin H, Cheng H (1997) Start-time fair queuing: a scheduling algorithm for integrated services packet switching networks. IEEE/ACM Trans Networking 5(5):690–704CrossRefGoogle Scholar
  12. 12.
    Kanhere SS, Sethu H, Parekh AB (2002) Fair and efficient packet scheduling using elastic round robin. IEEE Trans Parallel Distrib Syst 13(3):324–336CrossRefGoogle Scholar
  13. 13.
    Khamse-Ashari J, Kesidis G, Lambadaris I, Urgaonkar B, Zhao Y (2016) Constrained max-min fair scheduling of variable-length packet-flows to multiple servers. In: Proceedings IEEE GlobecomGoogle Scholar
  14. 14.
    Khamse-Ashari J, Kesidis G, Lambadaris I, Urgaonkar B, Zhao Y (2016) Max-min fair scheduling of variable-length packet-flows to multiple servers by deficit round-robin. In: Proceedings CISS, PrinctonGoogle Scholar
  15. 15.
    Khamse-Ashari J, Kesidis G, Lambadaris I, Urgaonkar B, Zhao Y (2017) Efficient and fair scheduling of placement constrained threads on heterogeneous multi-processors. In: Proceedings IEEE DCPerf. AtlantaGoogle Scholar
  16. 16.
    Khamse-Ashari J, Lambadaris I, Kesidis G, Urgaonkar B, Zhao Y (2017) Per-server dominant-share fairness (ps-dsf): a multiresource fair allocation mechanism for heterogeneous servers. In: Proceedings ICC. ParisGoogle Scholar
  17. 17.
    Khamse-Ashari J, Lambadaris I, Zhao YQ (2016) Constrained multi-user multi-server max-min fair queuing. arXiv:1601.04749
  18. 18.
    Lenzini L, Mingozzi E, Stea G (2002) Aliquem: a novel DRR implementation to achieve better latency and fairness at O(1) complexity. In: Proceedings IWQosGoogle Scholar
  19. 19.
    Mo J, Walrand J (2000) Fair end-to-end window-based congestion control. IEEE Trans Netw Serv Manag 8(5):556–567CrossRefGoogle Scholar
  20. 20.
    Parekh A, Gallager R (1993) A generalized processor sharing approach to flow control in integrated services networks: the single-node case. IEEE/ACM Trans Networking 1:344–357CrossRefGoogle Scholar
  21. 21.
    Parekh AK, Gallager RG (1993) A generalized processor sharing approach to flow control in integrated services networks: the single-node case. IEEE/ACM Trans Networking 1(3):344–357CrossRefGoogle Scholar
  22. 22.
    Shreedhar M, Varghese G (1996) Efficient fair queueing using deficit round-robin. IEEE/ACM Trans Networking 4(3):375– -385CrossRefGoogle Scholar
  23. 23.
    Wang W, Li B, Liang B, Li J (2016) Multi-resource fair sharing for datacenter jobs with placement constraints. In: Proceedings international conference for high performance computingGoogle Scholar
  24. 24.
    Yap K (2013) Using all wireless networks around us. PhD Thesis, Stanford UniversityGoogle Scholar
  25. 25.
    Yap K, Huang T, Yiakoumis Y, Chinchali S (2013) Scheduling packets over multiple interfaces while respecting user preferences. In: Proceedings ACM coNEXTGoogle Scholar

Copyright information

© Institut Mines-Télécom and Springer-Verlag France SAS 2017

Authors and Affiliations

  1. 1.SCE DepartmentCarleton UniversityOttawaCanada
  2. 2.School of EECSPennsylvania State UniversityState CollegeUSA
  3. 3.School of Mathematics and StatisticsCarleton UniversityOttawaCanada

Personalised recommendations