@inproceedings{af0eca63ca374c9b879ffebd137390a0,
title = "Prediction and control of short-term congestion in ATM networks using artificial intelligence techniques",
abstract = "Nowadays high-speed transmissions and heterogeneous traffic are some of the most essential requirements that a communication network must satisfy. Therefore, the design and management of such networks must consider these requirements. Network congestion is a very important point that must be taken into consideration when a management system is designed. ATM networks support different types of services and this fact makes them less predictable networks. Congestion can be defined as a state of network elements in which the network cannot guarantee the established connections the negotiated QoS. This paper proposes a system to reduce short-term congestion in ATM networks. This system uses Artificial Intelligence techniques to predict future states of network congestion in order to take less drastic measures in advance.",
author = "Guiomar Corral and Agust{\'i}n Zaballos and Joan Camps and Garrell, {Josep M.}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 1st International Conference on Networking, ICN 2001 ; Conference date: 09-07-2001 Through 13-07-2001",
year = "2001",
doi = "10.1007/3-540-47734-9_64",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "648--657",
editor = "Pascal Lorenz",
booktitle = "Networking - ICN 2001 - 1st International Conference on Networking, Proceedings",
}