Analysis of neural-network-based congestion control algorithms for ATM networks

نویسندگان

  • Christos Douligeris
  • Brajesh Kumar Singh
چکیده

Congestion control provides a challenge in the design of Asynchronous Transfer Mode (ATM) networks. Several algorithms have been proposed in the literature for alleviating or reducing congestion. In this paper the Jumping Window (JW), Triggered Jumping Window (TJW) and the Exponentially Weighted Moving Average (EWMA) window algorithms are analyzed, based on a closed-loop predictive feedback mechanism using a neural network. Singleand multiple-source models with real-world and simulated data are used to test the performance of the proposed mechanisms. The consequences of delayed feedback messages is also considered. Results indicate that neural controllers are e€ective in reducing the cell loss rate, while introducing minimal additional delays. # 1999 Elsevier Science Ltd. All rights reserved.

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تاریخ انتشار 1999