Cluster Computing

, Volume 21, Issue 4, pp 1881–1897 | Cite as

CECT: computationally efficient congestion-avoidance and traffic engineering in software-defined cloud data centers

  • M. M. Tajiki
  • B. Akbari
  • M. Shojafar
  • S. H. Ghasemi
  • M. L. Barazandeh
  • N. Mokari
  • L. Chiaraviglio
  • M. Zink


The proliferation of cloud data center applications and network function virtualization (NFV) boosts dynamic and QoS dependent traffic into the data centers network. Currently, lots of network routing protocols are requirement agnostic, while other QoS-aware protocols are computationally complex and inefficient for small flows. In this paper, a computationally efficient congestion avoidance scheme, called CECT, for software-defined cloud data centers is proposed. The proposed algorithm, CECT, not only minimizes network congestion but also reallocates the resources based on the flow requirements. To this end, we use a routing architecture to reconfigure the network resources triggered by two events: (1) the elapsing of a predefined time interval, or, (2) the occurrence of congestion. Moreover, a forwarding table entries compression technique is used to reduce the computational complexity of CECT. In this way, we mathematically formulate an optimization problem and define a genetic algorithm to solve the proposed optimization problem. We test the proposed algorithm on real-world network traffic. Our results show that CECT is computationally fast and the solution is feasible in all cases. In order to evaluate our algorithm in term of throughput, CECT is compared with ECMP (where the shortest path algorithm is used as the cost function). Simulation results confirm that the throughput obtained by running CECT is improved up to 3× compared to ECMP while packet loss is decreased up to 2×.


QoS-aware resource reallocation Traffic engineering Software-defined cloud data centers (SCDC) Network reprogramming overhead 


  1. 1.
    Mininet: Accessed 30 June 2017
  2. 2.
    Martini, L., Rosen, E., El-Aawar, N., Heron, G.: Ieee standard for local and metropolitan area networks—virtual bridged local area networks amendment 13: Congestion notification. In: IEEE Std 802.1Qau-2010 (Amendment to IEEE Std 802.1Q-2005) pp. c1-119 (2010)Google Scholar
  3. 3.
    Do, M.T., Jin, J., Wang, H., Man, Z.: Sliding mode learning based congestion control for diffserv networks. IET Control Theory Appl. 10(11), 1281–1287 (2016)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Su, W., Lagoa, C.M., Che, H.: Optimization-based, qos-aware distributed traffic control laws for networks with time-varying link capacities. Automatica 72, 158–165 (2016)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Otoshi, T., Ohsita, Y., Murata, M., Takahashi, Y., Ishibashi, K., Shiomoto, K., Hashimoto, T.: Traffic engineering based on stochastic model predictive control for uncertain traffic change. In: IFIP/IEEE International Symposium on Integrated Network Management (IM), Ottawa, Canada, pp. 1165–1170 (2015)Google Scholar
  6. 6.
    Lu, Y., Zhu, S.: Sdn-based tcp congestion control in data center networks. In: IEEE 34th International Performance, Computing and Communications Conference (IPCCC), Nanjing, China, pp. 1–7 (2015)Google Scholar
  7. 7.
    Gholami, M., Akbari, B.: Congestion control in software defined data center networks through flow rerouting. In: 23rd Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran, pp. 654–657 (2015)Google Scholar
  8. 8.
    Tajiki, M.M., Akbari, B., Mokari, N.: Optimal qos-aware network reconfiguration in software defined cloud data centers. Comput. Netw. 120, 71–86 (2017)CrossRefGoogle Scholar
  9. 9.
    Tajiki, M.M., Akbari, B., Mokari, N.: Qrtp: Qos-aware resource reallocation based on traffic prediction in software defined cloud networks. In: 8th IEEE International Symposium on Telecommunications (IST), Tehran, Iran, pp. 527–532 (2016)Google Scholar
  10. 10.
    Shetty, S., Yuchi, X., Song, M.: Optimizing network-aware resource allocation in cloud data centers. In: Moving Target Defense for Distributed Systems, pp. 43–55. Springer, New York (2016)CrossRefGoogle Scholar
  11. 11.
    Zhang, L., Tizghadam, A., Bannazadeh, H., Leon-Garcia, A.: Iterative traffic engineering in the data plane of multimedia ip communications. In: 2nd IEEE NetSoft Conference and Workshops (NetSoft), Seoul, South Korea, pp. 107–111 (2016)Google Scholar
  12. 12.
    Mushtaq, M.S., Fowler, S., Mellouk, A., Augustin, B.: Qoe/qos-aware lte downlink scheduler for voip with power saving. J. Netw. Comput. Appl. 51, 29–46 (2015)CrossRefGoogle Scholar
  13. 13.
    Egilmez, H.E., Civanlar, S., Tekalp, A.M.: A distributed qos routing architecture for scalable video streaming over multi-domain openflow networks. In: 19th IEEE International Conference on Image Processing (ICIP), Orlando, FL, USA, pp. 2237–2240 (2012)Google Scholar
  14. 14.
    Egilmez, H.E., Civanlar, S., Tekalp, A.M.: An optimization framework for qos-enabled adaptive video streaming over openflow networks. IEEE Trans. Multimed. 15(3), 710–715 (2013)CrossRefGoogle Scholar
  15. 15.
    Egilmez, H.E., Dane, S.T., Bagci, K.T., Tekalp, A.M.: Openqos: an openflow controller design for multimedia delivery with end-to-end quality of service over software-defined networks. In: Asia-Pacific, Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), Hollywood, CA, USA, pp. 1–8 (2012)Google Scholar
  16. 16.
    Egilmez, H.E., Gorkemli, B., Tekalp, A.M., Civanlar, S.: Scalable video streaming over openflow networks: An optimization framework for qos routing. In: 18th IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, pp. 2241–2244 (2011)Google Scholar
  17. 17.
    Shojafar, M., Canali, C., Lancellotti, R., Abawajy, J.: Adaptive computing-plus-communication optimization framework for multimedia processing in cloud systems. IEEE Trans. Cloud Comput. (2016)
  18. 18.
    Civanlar, S., Parlakisik, M., Tekalp, A.M., Gorkemli, B., Kaytaz, B., Onem, E.: A qos-enabled openflow environment for scalable video streaming. In: IEEE GLOBECOM Workshops (GC Wkshps), Miami, FL, USA, pp. 351–356 (2010)Google Scholar
  19. 19.
    Ghosh, A., Ha, S., Crabbe, E., Rexford, J.: Scalable multi-class traffic management in data center backbone networks. IEEE J. Sel. Areas Commun. 31(12), 2673–2684 (2013)CrossRefGoogle Scholar
  20. 20.
    Kulkarni, S., Sharma, R., Mishra, I.: New qos routing algorithm for mpls networks using delay and bandwidth constraints. Int. J. Inf. 2(3), 285–293 (2012)Google Scholar
  21. 21.
    Leela, R., Thanulekshmi, N., Selvakumar, S.: Multi-constraint qos unicast routing using genetic algorithm (muruga). Appl. Soft Comput. 11(2), 1753–1761 (2011)CrossRefGoogle Scholar
  22. 22.
    Liang, B., Yu, J.: One multi-constraint qos routing algorithm cgea based on ant colony system. In: 2nd International Conference on Information Science and Control Engineering (ICISCE), Shanghai, China, pp. 848–851 (2015)Google Scholar
  23. 23.
    Ongaro, F.: Enhancing quality of service in software-defined networks. Ph.D. Thesis, University of Bologna. (2014)
  24. 24.
    Wang, J.M., Wang, Y., Dai, X., Bensaou, B.: Sdn-based multi-class qos-guaranteed inter-data center traffic management. In: IEEE 3rd International Conference on Cloud Networking (CloudNet), Luxembourg, pp. 401–406 (2014)Google Scholar
  25. 25.
    Zhao, J., Ge, X.: Qos multi-path routing scheme based on acr algorithm in industrial ethernet. In: Third International Conference on Communications, Signal Processing, and Systems, pp. 593–601 (2015)Google Scholar
  26. 26.
    Benson, T.A.: New paradigms for managing the complexity and improving the performance of enterprise networks. Ph.D. Thesis, The University of Wisconsin-Madison (2012)Google Scholar
  27. 27.
    Guck, J.W., Reisslein, M., Kellerer, W.: Function split between delay-constrained routing and resource allocation for centrally managed qos in industrial networks. IEEE Trans. Ind. Inform. 12(6), 2050–2061 (2016)CrossRefGoogle Scholar
  28. 28.
    Eiben, A.E., Raue, P.E., Ruttkay, Z.: Genetic algorithms with multi-parent recombination. In: International Conference on Parallel Problem Solving from Nature, pp. 78–87. Springer, New York (1994)CrossRefGoogle Scholar
  29. 29.
    Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)zbMATHGoogle Scholar
  30. 30.
    Benson, T.A.: New paradigms for managing the complexity and improving the performance of enterprise networks. Ph.D. Thesis, University of Wisconsin-Madison (2012)Google Scholar
  31. 31.
    Fast proportional selection: Accessed June 2017
  32. 32.
    Abu-Lebdeh, G., Benekohal, R.F.: Convergence variability and population sizing in micro-genetic algorithms. Comput. Aided Civil Infrastruct. Eng. 14(5), 321–334 (1999)CrossRefGoogle Scholar
  33. 33.
    Leiserson, C.E.: Fat-trees: universal networks for hardware-efficient supercomputing. IEEE Trans. Comput. 100(10), 892–901 (1985)CrossRefGoogle Scholar
  34. 34.
    Benson, T., Akella, A., Maltz, D.A.: Network traffic characteristics of data centers in the wild. In: 10th ACM SIGCOMM conference on Internet measurement, Melbourne, Australia, pp. 267–280 (2010)Google Scholar
  35. 35.
    PoX: Accessed 30 June 2017

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • M. M. Tajiki
    • 1
  • B. Akbari
    • 1
  • M. Shojafar
    • 2
  • S. H. Ghasemi
    • 1
  • M. L. Barazandeh
    • 1
  • N. Mokari
    • 1
  • L. Chiaraviglio
    • 2
  • M. Zink
    • 3
  1. 1.Electrical and Computer EngineeringTarbiat Modares UniversityTehranIran
  2. 2.CNIT, Department of Electronic EngineeringTor Vergata University of RomeRomeItaly
  3. 3.Electrical and Computer EngineeringUniversity of Massachusetts AmherstAmherstUSA

Personalised recommendations