標題: PSD-based neural-net connection admission control
作者: Chang, CJ
Lin, SY
Cheng, RG
Shiue, YR
交大名義發表
電信工程研究所
National Chiao Tung University
Institute of Communications Engineering
公開日期: 1997
摘要: 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. 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. In this paper, 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 an amount of 18%, while keeping the QoS contract.
URI: http://hdl.handle.net/11536/19627
ISBN: 0-8186-7780-5
ISSN: 0743-166X
期刊: IEEE INFOCOM '97 - THE CONFERENCE ON COMPUTER COMMUNICATIONS, PROCEEDINGS, VOLS 1-3: SIXTEENTH ANNUAL JOINT CONFERENCE OF THE IEEE COMPUTER AND COMMUNICATIONS SOCIETIES - DRIVING THE INFORMATION REVOLUTION
起始頁: 955
結束頁: 962
顯示於類別:會議論文