Queuing Model for SIP Server Hysteretic Overload Control with Bursty Traffic

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8121)


In this paper, we develop a mathematical model of a load control mechanism for SIP server signaling networks based on a hysteretic technique. We investigate loss-based overload control, as proposed in recent IETF documents. The queuing model takes into account three types of system state – normal load, overload, and discard. The hysteretic control is made possible by introducing two thresholds, L and H, in the buffer of total size R. We denote the mathematical model using the modified Kendall notation as an \(MMPP|M|1|\left\langle L,H\right\rangle |R\) queue with hysteretic load control and bursty input flow. Algorithms for computation the key performance parameters of the system were obtained. A numerical example illustrating the control mechanism that minimizes the return time from overloading states satisfying the throttling and mean control cycle time constraints is also presented.


SIP server hop-by-hop overload control loss-based overload control hysteretic control return time queuing model MMPP flow 


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  1. 1.
    Rosenberg, J., Schulzrinne, H., Camarillo, G., et al.: SIP: Session Initiation Protocol. RFC3261 (2002)Google Scholar
  2. 2.
    Rosenberg, J.: Requirements for Management of Overload in the Session Initiation Protocol. RFC5390 (2008)Google Scholar
  3. 3.
    Hilt, V., Noel, E., Shen, C., Abdelal, A.: Design Considerations for Session Initiation Protocol (SIP) Overload Control. RFC6357 (2011)Google Scholar
  4. 4.
    Gurbani, V., Hilt, V., Schulzrinne, H.: Session Initiation Protocol (SIP) Overload Control. draft-ietf-soc-overload-control-08 (2012)Google Scholar
  5. 5.
    Abaev, P., Gaidamaka, Y., Samouylov, K.E.: Modeling of Hysteretic Signaling Load Control in Next Generation Networks. In: Andreev, S., Balandin, S., Koucheryavy, Y. (eds.) NEW2AN/ruSMART 2012. LNCS, vol. 7469, pp. 440–452. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Bali, S., Victor, S.: Frost An Algorithm for Fitting MMPP to IP Traffic Traces. IEEE Communication Letters 11(2), 207–209 (2007)CrossRefGoogle Scholar
  7. 7.
    Abaev, P., Gaidamaka, Y., Pechinkin, A., Razumchik, R., Shorgin, S.: Simulation of overload control in SIP server networks. In: Proc. of the 26th European Conference on Modelling and Simulation, ECMS 2012, pp. 533–539 (2012)Google Scholar
  8. 8.
    Abaev, P., Pechinkin, A., Razumchik, R.: On analytical model for optimal sip server hop-by-hop overload control. In: Proc. of the 4th International Congress on Ultra Modern Telecommunications and Control Systems, ICUMT 2012, pp. 303–308 (2012)Google Scholar
  9. 9.
    Abaev, P., Pechinkin, A., Razumchik, R.: Analysis of queueing system with constant service time for SIP server hop-by-hop overload control. In: Dudin, A., Klimenok, V., Tsarenkov, G., Dudin, S. (eds.) BWWQT 2013. CCIS, vol. 356, pp. 1–10. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Bocharov, P.P., DÁpice, C., Pechinkin, A.V., Salerno, S.: Queueing theory. Series ”Modern Probability and Statistics”. VSP Publishing, Utrecht (2003)CrossRefGoogle Scholar
  11. 11.
    Latouche, G., Ramaswami, V.: Introduction to matrix analytic methods in stochastic modeling. SIAM, Philadelphia (1999)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Telecommunication Systems DepartmentPeoples’ Friendship University of RussiaMoscowRussia
  2. 2.Institute of Informatics Problems of Russian Academy of SciencesMoscowRussia

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