The Optimization of Traffic Management for Cloud Application and Services in the Virtual Data Center

  • Irina Bolodurina
  • Denis ParfenovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10421)


Nowadays one of the problems of optimization is the control of the traffic in cloud applications and services in the network environment of virtual data center. Taking into account the multitier architecture of modern data centers, we need to pay a special attention to this task. The advantage of modern infrastructure virtualization is the possibility to use software-defined networks and software-defined data storages. However, the existing optimization of algorithmic solutions does not take into account the specific features of the heterogeneous network traffic routing with multiple application types. The task of optimizing traffic distribution for cloud applications and services can be solved by using software-defined infrastructure of virtual data centers. We have developed a simulation model for the traffic in software-defined networks segments of virtual data centers involved in processing user requests to cloud application and services within a network environment. Our model enables to implement the traffic management algorithm of cloud applications and optimize the access to storage systems through the effective use of data transmission channels. During the experimental studies, we have found that the use of our algorithm enables to decrease the response time of cloud applications and services and, therefore, increase the productivity of user requests processing and reduce the number of refusals.


Software-Defined Network Virtual Data Center Cloud computing Traffic Simulation model Software-defined infrastructure 



The research has been supported by the Russian Foundation of Basic Research (grants 16-37-60086 mol_a_dk, 16-07-01004 a), and the President of the Russian Federation within the grant for state support of young Russian scientists (MK-1624.2017.9).


  1. 1.
    Bolodurina, I., Parfenov, D.: Development and research of models of organization storages based on the software-defined infrastructure. In: 39th International Conference on Telecommunications and Signal Processing, pp. 1–6. IEEE Press, Vienna (2016). doi: 10.1109/TSP.2016.7760818
  2. 2.
    Parfenov, D., Bolodurina, I., Shukhman, A.: Approach to the effective controlling cloud computing resources in data centers for providing multimedia services. In: International Siberian Conference on Control and Communications, pp. 1–6. IEEE Press, Omsk (2015). doi: 10.1109/SIBCON.2015.7147170
  3. 3.
    Garey, M., Graham, R.: Bounds for multiprocessor scheduling with resource constraints. SIAM J. Comput. 4(2), 187–200 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Arndt, O., Freisleben, B., Kielmann, T., Thilo, F.: A comparative study of online scheduling algorithms for networks of workstations. Cluster Comput. 3(2), 95–112 (2000)CrossRefGoogle Scholar
  5. 5.
    Feitelson, D., Weil, A.: Utilization and predictability in scheduling the IBM SP2 with backfilling. In: Parallel Processing Symposium, pp. 45–52 (1998)Google Scholar
  6. 6.
    Lawson, B.G., Smirni, E.: Multiple-queue backfilling scheduling with priorities and reservations for parallel systems. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2002. LNCS, vol. 2537, pp. 72–87. Springer, Heidelberg (2002). doi: 10.1007/3-540-36180-4_5 CrossRefGoogle Scholar
  7. 7.
    Srinivasan, S., Kettimuthu, R., Subramani, V., Sadayappan, P.: Selective reservation strategies for backfill job scheduling. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2002. LNCS, vol. 2537, pp. 55–71. Springer, Heidelberg (2002). doi: 10.1007/3-540-36180-4_4 CrossRefGoogle Scholar
  8. 8.
    Li, J., Li, M., Wang, G., Liu, X., Li, Z., Tang, H.: Global reliability evaluation for cloud storage systems with proactive fault tolerance. In: Wang, G., Zomaya, A., Perez, G.M., Li, K. (eds.) ICA3PP 2015. LNCS, vol. 9531, pp. 189–203. Springer, Cham (2015). doi: 10.1007/978-3-319-27140-8_14 CrossRefGoogle Scholar
  9. 9.
    Rahme, J., Xu, H.: Reliability-based software rejuvenation scheduling for cloud-based systems. In: The 27th International Conference on Software Engineering and Knowledge Engineering, pp. 1–6 (2015)Google Scholar
  10. 10.
    Lin, T.: Enabling SDN applications on software-defined infrastructure. In: Network Operations and Management Symposium (NOMS), pp. 1–7. IEEE Press (2011)Google Scholar
  11. 11.
    Ibanez, G., Naous, J., Rojas, E., Rivera, D., Schuymer, T.: A small data center network of ARP-path bridges made of openflow switches. In: The 36th IEEE Conference on Local Computer Networks (LCN), pp. 15–23. IEEE Press (2011)Google Scholar
  12. 12.
    Tavakoli, A., Casado, M., Koponen, T., Shenker, S.: Applying NOX to the datacenter. In: 8th ACM Workshop on Hot Topics in Networks (HotNets-VIII). IEEE Press, New York (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Orenburg State UniversityOrenburgRussia

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