, Volume 98, Issue 1–2, pp 147–168 | Cite as

Adaptive scheduling algorithm for media-optimized traffic management in software defined networks

  • Florin Pop
  • Ciprian DobreEmail author
  • Dragos Comaneci
  • Joanna Kolodziej


Multi-policy resource management have been considered as an efficient methodology for delivering ready-to-use media-optimized applications in Software-Defined Networks (SDNs). Prioritized flow scheduling ensures high-speed communication in SDNs under large-scale distribution, heterogeneity of network resources, and exponential distribution of the flows granularity. The effectiveness of priority-based approaches depends usually on the control mechanism of the resource management. In this paper we improve the resource utilization by developing a novel adaptive scheduling strategy. We came with an effecting scheduling strategy to determine what resource to be allocated to a set of flows keeping their priority, increasing the average utilization of resources and, most importantly, establishing a virtual circuit for a specific flow over a network. Our theoretical remarks and extensive simulation results show that the proposed scheduling strategies can achieve the described goals.


Scheduling Flow management Adaptive methods  Media-optimized applications Software-Defined Networking 

Mathematics Subject Classification (2010)

68M20 68M14 68U20 



The research presented in this paper is supported by the following projects: “ERRIC-Empowering Romanian Research on Intelligent Information Technologies”, FP7-REGPOT-2010-1, ID: 264207; “SideSTEP-Scheduling Methods for Dynamic Distributed Systems: a self-* approach”,(PN-II-CT-RO-FR-2012-1-0084); “CyberWater” grant of the Romanian National Authority for Scientific Research, CNDI-UEFISCDI, project number 47/2012. We would like to thank the reviewers for their time and expertise, constructive comments and valuable insights.


  1. 1.
    Jacobson V (1988) Congestion avoidance and control. ACM SIGCOMM Computer Communication Review, vol 18. ACM, New York, pp 314–329Google Scholar
  2. 2.
    Demers A, Keshav S, Shenker S (1989) Analysis and simulation of a fair queueing algorithm. ACM SIGCOMM Comput Commun Rev 19(4):1–12CrossRefGoogle Scholar
  3. 3.
    McKenney PE (1990) Stochastic fairness queueing. In: Proceedings of Ninth Annual Joint Conference of the IEEE Computer and Communication Societies, IEEE INFOCOM’90. The Multiple Facets of Integration, pp 733–740Google Scholar
  4. 4.
    Floyd S, Jacobson V (1993) Random early detection gateways for congestion avoidance. IEEE/ACM Trans Netw 1(4):397–413CrossRefGoogle Scholar
  5. 5.
    Feng W-c, Shin KG, Kandlur DD, Saha D (2002) The BLUE active queue management algorithms. IEEE/ACM Trans Netw 10(4):513–528CrossRefGoogle Scholar
  6. 6.
    Floyd S (1994) TCP and explicit congestion notification. ACM SIGCOMM Comput Commun Rev 24(5):8–23MathSciNetCrossRefGoogle Scholar
  7. 7.
    Katabi D, Handley M, Rohrs C (2002) Congestion control for high bandwidth delay product networks. ACM SIGCOMM Comput Commun Rev 32(4):89–102CrossRefGoogle Scholar
  8. 8.
    Tai CH, Zhu J, Dukkipati N (2008) Making large scale deployment of RCP practical for real networks. In: The 27th Conference on Computer Communications INFOCOM 2008, IEEE, pp 2180–2188Google Scholar
  9. 9.
    Alizadeh M, Greenberg A, Maltz DA, Padhye J, Patel P, Prabhakar B, Sengupta S, Sridharan M (2010) Data center TCP (DCTCP). ACM SIGCOMM Comput Commun Rev 40(4):63–74CrossRefGoogle Scholar
  10. 10.
    Hong CY, Caesar M, Godfrey P (2012) Finishing flows quickly with preemptive scheduling. ACM SIGCOMM Comput Commun Rev 42(4):127–138CrossRefGoogle Scholar
  11. 11.
    Nichols K, Jacobson V (2012) Controlling queue delay. Commun ACM 55(7):42–50CrossRefGoogle Scholar
  12. 12.
    Alizadeh M, Yang S, Sharif M, Katti S, McKeown N, Prabhakar B, Shenker S (2013) pFabric: Minimal near-optimal datacenter transport. In: Proceedings of the ACM SIGCOMM 2013, pp 435-446Google Scholar
  13. 13.
    Wang L, Khan SU, Chen D, Koodziej J, Ranjan R, Zomaya A (2013) Energy-aware parallel task scheduling in a cluster. Future Gener Comput Syst 29(7):1661–1670CrossRefGoogle Scholar
  14. 14.
    Sivaraman A, Winstein K, Subramanian S, Balakrishnan H (2013) No silver bullet: extending SDN to the data plane. In: Twelfth ACM Workshop on Hot Topics in Networks (HotNets-XII). College ParkGoogle Scholar
  15. 15.
    Ha S, Rhee I (2008) CUBIC: a new TCP-friendly high-speed TCP variant. ACM SIGOPS Oper Syst Rev 42(5):64–74CrossRefGoogle Scholar
  16. 16.
    Song KTJ, Zhang Q, Sridharan M (2006) Compound TCP: a scalable and TCP-friendly congestion control for high-speed networks. In: Proceedings of PFLDnet 2006Google Scholar
  17. 17.
    Ma Y, Wang L (2013) Task-tree based large-scale Mosaicking for remote sensed imageries with dynamic DAG scheduling. IEEE Transactions on Parallel and Distributed Systems (Published Online 20 Nov 2013)Google Scholar
  18. 18.
    Rahman M, Ranjan R, Buyya R, Benatallah B (2011) A taxonomy and survey on autonomic management of applications in grid computing environments. Concurrency and computation: practice and experience 23(16):1990–2019CrossRefGoogle Scholar
  19. 19.
    Casado M, Freedman MJ, Pettit J, Luo J, McKeown N, Shenker S (2007) Ethane: taking control of the enterprise. ACM SIGCOMM Comput Commun Rev 37(4):1–12CrossRefGoogle Scholar
  20. 20.
    Hui P, Koponen T, Hui P, Koponen T (2012) Software defined networking (Dagstuhl seminar 12363). Dagstuhl Rep 2(9):95–108Google Scholar
  21. 21.
    Crowcroft J, Fidler M, Nahrstedt K, Steinmetz R (2013) Is SDN the de-constraining constraint of the future internet? ACM SIGCOMM Comput Commun Rev 43(5):13–18CrossRefGoogle Scholar
  22. 22.
    Benson T, Akella A, Maltz DA (2010) Network traffic characteristics of data centers in the wild. In: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement. ACM, New York, pp 267–280Google Scholar
  23. 23.
    Greenberg A, Lahiri P, Maltz DA, Patel P, Sengupta S (2008) Towards a next generation data center architecture: scalability and commoditization. In: Proceedings of the ACM workshop on Programmable routers for extensible services of tomorrow. ACM, New York, pp 57–62Google Scholar
  24. 24.
    Hwang FK (1972) Rearrangeability of multi-connection three-stage Clos networks. Networks 2(4):301–306MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Cisco Systems. Cisco’s Massively Scalable Data Center (2013). Accessed November 14 2013
  26. 26.
    Wang L, Tao J, Ranjan R, Marten H, Streit A, Chen J, Chen D (2013) G-Hadoop: MapReduce across distributed data centers for data-intensive computing. Future Gener Comput Syst 29(3):739–750CrossRefGoogle Scholar
  27. 27.
    Hedlund Brad Starting a new journey with dell force10 (2011). Accessed November 14th 2013
  28. 28.
    Chen M, Jin H, Wen Y, Leung VCM (2013) Enabling technologies for future data center networking: a primer. IEEE Netw 27(4):8–15. doi: 10.1109/MNET.2013.6574659 CrossRefGoogle Scholar
  29. 29.
    Benson T, Akella A, Maltz DA (2010) Network traffic characteristics of data centers in the wild. In: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement (IMC ’10). ACM, New York, pp 267–280. doi: 10.1145/1879141.1879175
  30. 30.
    McKeown N, Anderson T, Balakrishnan H, Parulkar G, Peterson L, Rexford J, Shenker S, Turner J (2008) OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Comput Commun Rev 38(2):69–74CrossRefGoogle Scholar
  31. 31.
    Gude N, Koponen T, Pettit J, Pfaff B, Casado M, McKeown N, Shenker S (2008) NOX: towards an operating system for networks. ACM SIGCOMM Comput Commun Rev 38(3):105–110CrossRefGoogle Scholar
  32. 32.
    Curtis A, Mogul J, Tourrilhes J, Yalagandula P, Sharma P, Banerjee S (2011) DevoFlow: Scaling flow management for high-performance networks. ACM SIGCOMM Comput Commun Rev 41(4):254–265CrossRefGoogle Scholar
  33. 33.
    Caesar M, Caldwell D, Feamster N, Rexford J, Shaikh A, van der Merwe J (2005) Design and implementation of a routing control platform. In: Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation, vol 2. USENIX Association, pp 15–28Google Scholar
  34. 34.
    Greenberg A, Hjalmtysson G, Maltz DA, Myers A, Rexford J, Xie G, Yan H, Zhan J, Zhang H (2005) A clean slate 4D approach to network control and management. ACM SIGCOMM Comput Commun Rev 35(5):41–54CrossRefGoogle Scholar
  35. 35.
    Casado M, Garfinkel T, Aditya A, Freedman MJ, Boneh D, McKeown N, Shenker S (2006) SANE: A protection architecture for enterprise networks. In: USENIX Security SymposiumGoogle Scholar
  36. 36.
    Rothenberg CE, Nascimento MR, Salvador MR, Araujo CN, Cunha de Lucena S, Raszuk R (2012) Revisiting routing control platforms with the eyes and muscles of software-defined networking. In: Proceedings of the first workshop on Hot topics in software defined networks. ACM, New York, pp 13–18Google Scholar
  37. 37.
    Mocanu M, Craciun A (2012) Monitoring watershed parameters through Software services. In: 2012 Third International Conference on Emerging Intelligent Data and Web Technologies (EIDWT), pp 287–292Google Scholar
  38. 38.
    Mocanu M, Vacariu L, Drobot R, Muste M (2013) Information-centric systems for supporting decision-making in watershed resource development. In: 2013 19th International Conference onControl Systems and Computer Science (CSCS), pp 611–616Google Scholar
  39. 39.
    Ke BY, Tien PL, Hsiao YL (2013) Parallel prioritized flow scheduling for software defined data center network. In: 2013 IEEE 14th International Conference on High Performance Switching and Routing (HPSR), pp 217–218Google Scholar
  40. 40.
    Al-Fares M, Radhakrishnan S, Raghavan B, Huang N, Vahdat A. Hedera (2010) Dynamic flow scheduling for data center networks. In: Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation, NSDI’10. USENIX Association, Berkeley, CA, USA, pp 19–19Google Scholar
  41. 41.
    Ferguson AD, Guha A, Liang C, Fonseca R, Krishnamurthi S (August 2013) Participatory networking: an API for application control of SDNs. SIGCOMM Comput Commun Rev 43(4):327–338Google Scholar
  42. 42.
    Sen S, Shue D, Ihm S, Freedman MJ (2013) Scalable, optimal flow routing in datacenters via local link balancing. In: Proceedings of the Ninth ACM Conference on Emerging Networking Experiments and Technologies, CoNEXT ’13. ACM, New York, pp 151–162Google Scholar
  43. 43.
    Jain S, Kumar A, Mandal S,Ong J, Poutievski L, Singh A, Venkata S, Wanderer J, Zhou J, Zhu M et al. (2013) B4: Experience with a globally-deployed software defined WAN. In: Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM. ACM, New York, pp 3–14Google Scholar
  44. 44.
    Heller B, Seetharaman S, Mahadevan P, Yiakoumis Y, Sharma P, Banerjee S, McKeown N (2010) ElasticTree: saving energy in data center networks. NSDI 3:19–21Google Scholar
  45. 45.
    Ng C-H, Boon-He S (2002) Queueing modelling fundamentals. Wiley, ChichesterGoogle Scholar
  46. 46.
    Serbanescu C (1998) Stochastic differential equations and unitary processes. Bull Math Soc Sc Math Roumanie Tome 41 89(3):311–322Google Scholar
  47. 47.
    Gardiner CW (1985) Handbook of stochastic methods. Springer, BerlinGoogle Scholar
  48. 48.
    Serbanescu C (1998) Noncommutative Markov processes as stochastic equations’ solutions. Bull Math Soc Sc Math Roumanie Tome 41 89(3):219–228MathSciNetGoogle Scholar
  49. 49.
    Izakian H, Abraham A, Snášel V (2009) Performance comparison of six efficient pure heuristics for scheduling meta-tasks on heterogeneous distributed environments. Neural Netw World 19(6):695–710Google Scholar

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Florin Pop
    • 1
  • Ciprian Dobre
    • 1
    Email author
  • Dragos Comaneci
    • 1
  • Joanna Kolodziej
    • 2
  1. 1.Computer Science Department, Faculty of Automatic Control and ComputersUniversity Politehnica of BucharestBucharestRomania
  2. 2.Institute of Computer ScienceCracow University of TechnologyKrakówPoland

Personalised recommendations