PSD-based neural-net connection admission control

ATM (asynchronous transfer mode) systems can support services with bursty traffic. An ATM system needs a sophisticated and real-time connection admission controller not only to guarantee the required quality-of-service (QoS) for existing calls but also to raise the system efficiency. The input process has a power-spectral-density (PSD) which explicitly contains the correlation behavior of input traffic and has a great impact on the system performance. Also, a neural network has been widely applied to deal with traffic control related problems in ATM systems because of its self-learning capability. We propose a PSD-based neural-net connection admission control (PNCAC) method for an ATM system. Under the QoS constraint, we construct a decision hyperplane of the connection admission control according to parameters of the power spectrum. We further adopt the learning/adapting capabilities of the neural network to adjust the optimum location of the boundary between these two decision spaces. Simulation results show that the PNCAC method provides a superior system utilization over the conventional CAC schemes by as much as 18%, while keeping the QoS contract.

[1]  Chung-Ju Chang,et al.  Design of a fuzzy traffic controller for ATM networks , 1996, TNET.

[2]  San-qi Li,et al.  Queue response to input correlation functions: discrete spectral analysis , 1993, TNET.

[3]  Roch Guérin,et al.  A unified approach to bandwidth allocation and access control in fast packet-switched networks , 1992, [Proceedings] IEEE INFOCOM '92: The Conference on Computer Communications.

[4]  Neil E. Cotter,et al.  The Stone-Weierstrass theorem and its application to neural networks , 1990, IEEE Trans. Neural Networks.

[5]  San-qi Li,et al.  Queue response to input correlation functions: continuous spectral analysis , 1993, TNET.

[6]  Hiroshi Saito,et al.  Call admission control in an ATM network using upper bound of cell loss probability , 1992, IEEE Trans. Commun..

[7]  Chung-Ju Chang,et al.  A neural-net based fuzzy admission controller for an ATM network , 1996, Proceedings of IEEE INFOCOM '96. Conference on Computer Communications.

[8]  P. Tran-Gia,et al.  Performance of a neural net used as admission controller in ATM systems , 1992, [Conference Record] GLOBECOM '92 - Communications for Global Users: IEEE.

[9]  Ibrahim Habib,et al.  A neural network control for effective admission control in ATM networks , 1996, Proceedings of ICC/SUPERCOMM '96 - International Conference on Communications.

[10]  Chung-Ju Chang,et al.  Neural-network connection-admission control for ATM networks , 1997 .

[11]  San-qi Li,et al.  On input state space reduction and buffer noneffective region , 1994, Proceedings of INFOCOM '94 Conference on Computer Communications.

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

[13]  Atsushi Hiramatsu,et al.  ATM communications network control by neural networks , 1990, IEEE Trans. Neural Networks.

[14]  San-qi Li,et al.  Predictive Dynamic Bandwidth Allocation for Efficient Transport of Real-Time VBR Video over ATM , 1995, IEEE J. Sel. Areas Commun..

[15]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[16]  Anindo Banerjea,et al.  The Tenet real-time protocol suite: design, implementation, and experiences , 1996, TNET.

[17]  Luigi Fratta,et al.  ATM: bandwidth assignment and bandwidth enforcement policies , 1989, IEEE Global Telecommunications Conference, 1989, and Exhibition. 'Communications Technology for the 1990s and Beyond.

[18]  Rainer Händel,et al.  ATM Networks: Concepts, Protocols, Applications , 1998 .

[19]  Chung-Ju Chang,et al.  A power-spectrum based connection admission control for ATM networks , 1996, Proceedings of ICC/SUPERCOMM '96 - International Conference on Communications.