The extended connection-dependent threshold model for call-level performance analysis of multi-rate loss systems under the bandwidth reservation policy

  • Authors:
  • Vassilios G. Vassilakis;Ioannis D. Moscholios;Michael D. Logothetis

  • Affiliations:
  • Wire Communications Laboratory Department of Electrical & Computer Engineering, University of Patras, 265 04, Patras, Greece;Department of Telecommunications Science and Technology, University of Peloponnese, Tripolis, Greece;Wire Communications Laboratory Department of Electrical & Computer Engineering, University of Patras, 265 04, Patras, Greece

  • Venue:
  • International Journal of Communication Systems
  • Year:
  • 2012

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Abstract

Firstly, we reviewed two extensions of the Erlang multi-rate loss model, whereby we can assess the call-level QoS of telecom networks supporting elastic traffic: (i) the extended Erlang multi-rate loss model, where random arriving calls of certain bandwidth requirements at call setup can tolerate bandwidth compression while in service; and (ii) the connection-dependent threshold model, where arriving calls may have several contingency bandwidth requirements, whereas in-service calls cannot tolerate bandwidth compression. Secondly, we proposed a new model, the extended connection-dependent threshold model. Calls may have alternative bandwidth requirements at call setup and can tolerate bandwidth compression while in service. We proposed a recurrent formula for the efficient calculation of link occupancy distribution and consequently call blocking probabilities, link utilization, and throughput per service class. Furthermore, in the proposed model, we incorporated the bandwidth reservation policy, whereby we can (i) equalize the call blocking probabilities of different service classes, (ii) guarantee specific QoS per service class, and (iii) implement different maximum bandwidth compression/expansion rate per service class so that the network supports both elastic and stream traffic. The accuracy of the new model is verified by simulation. Moreover, the proposed model performs better than the existing models. Finally, we generalize the proposed model by incorporating service classes with either random or quasi-random arrivals. Copyright © 2011 John Wiley & Sons, Ltd.