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.
CITATION STYLE
Corral, G., Zaballos, A., Camps, J., & Garrell, J. M. (2001). Prediction and control of short-term congestion in ATM networks using artificial intelligence techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2094, pp. 648–657). Springer Verlag. https://doi.org/10.1007/3-540-47734-9_64
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