A Decision-Theoretic Approach to Call Admission Control in ATM Networks

This paper describes a simple and robust ATM call admission control, and develops the theoretical background for its analysis. Acceptance decisions are based on whether the current load is less than a precalculated threshold, and Bayesian decision theory provides the framework for the choice of thresholds. This methodology allows an explicit treatment of the trade-off between cell loss and call rejection, and of the consequences of estimation error. Further topics discussed include the robustness of the control to departures from model assumptions, its performance relative to a control possessing precise knowledge of all unknown parameters, the relationship between leaky bucket depths and buffer requirements, and the treatment of multiple call types. >

[1]  Peter Key,et al.  Adaptive Call Admission Control in ATM Networks , 1994 .

[2]  Sally Floyd,et al.  Wide-Area Traffic: The Failure of Poisson Modeling , 1994, SIGCOMM.

[3]  Peter Key,et al.  Dimensioning playout buffers from an ATM network , 1994 .

[4]  Joseph Y. Hui Resource allocation for broadband networks , 1988, IEEE J. Sel. Areas Commun..

[5]  Abhay Parekh,et al.  A generalized processor sharing approach to flow control in integrated services networks: the single-node case , 1993, TNET.

[6]  Jean C. Walrand,et al.  Admission control and routing in ATM networks using inferences from measured buffer occupancy , 1995, IEEE Trans. Commun..

[7]  Richard J. Gibbens,et al.  Effective bandwidths for the multi-type UAS channel , 1991, Queueing Syst. Theory Appl..

[8]  Debasis Mitra,et al.  Effective bandwidth of general Markovian traffic sources and admission control of high speed networks , 1993, TNET.

[9]  J. Lehoczky,et al.  Insensitivity of blocking probabilities in a circuit-switching network , 1984 .

[10]  V. Schmidt,et al.  Queues and Point Processes , 1983 .

[11]  T. Kurtz,et al.  Large loss networks , 1994 .

[12]  Nigel G. Bean,et al.  Robust connection acceptance control for ATM networks with incomplete source information , 1994, Ann. Oper. Res..

[13]  P. Franken,et al.  Queues and Point Processes , 1983 .

[14]  Walter Willinger,et al.  Traffic modeling for high-speed networks: theory versus practice , 1995 .

[15]  Hamid Ahmadi,et al.  Equivalent Capacity and Its Application to Bandwidth Allocation in High-Speed Networks , 1991, IEEE J. Sel. Areas Commun..

[16]  R. Gibbens,et al.  Asymptotic analysis of single resource loss systems in heavy traffic, with applications to integrated networks , 1995, Advances in Applied Probability.

[17]  Peter Key Admission Control Problems in Telecommunications , 1995 .

[18]  Marcel F. Neuts,et al.  Methods for performance evaluation of VBR video traffic models , 1994, TNET.

[19]  Kohei Shiomoto,et al.  Dynamic Call Admission Control in ATM Networks , 1991, IEEE J. Sel. Areas Commun..

[20]  Frank P. Kelly,et al.  Effective bandwidths at multi-class queues , 1991, Queueing Syst. Theory Appl..

[21]  Maurice Yacowar,et al.  The Life and Work , 1993 .

[22]  Stan Zachary,et al.  The Performance of Single Resource Loss Systems in Multiservice Networks , 1994 .

[23]  M. Degroot,et al.  Probability and Statistics , 2021, Examining an Operational Approach to Teaching Probability.

[24]  Kenn S. Kvols,et al.  Source-Independent Call Acceptance Procedures in ATM Networks , 1991, IEEE J. Sel. Areas Commun..

[25]  James Roberts Virtual spacing for flexible traffic control , 1994 .

[26]  Rene L. Cruz,et al.  A calculus for network delay, Part I: Network elements in isolation , 1991, IEEE Trans. Inf. Theory.