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Use of adaptive resonance theory (ART) neural networks to compute bottleneck link speed in heterogeneous networking environments
Use of adaptive resonance theory (ART) neural networks to compute bottleneck link speed in heterogeneous networking environments
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机译：使用自适应共振理论（ART）神经网络来计算异构网络环境中的瓶颈链路速度
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摘要
Bottleneck link speed, or the transmission speed of the slowest link within a path between two nodes, is determining by transmitting a sequence of ICMP ECHO data packets from the source node to the target node at a selected interval and measuring the return data packet intervals. Rather than using statistical analysis methods, the return data packet interval measurements are input into an adaptive resonance theory neural network trained with the expected interval for every known, existing network transmission speed. The neural network will then classify the return data packet interval measurements, indicating the bottleneck link speed. Since most of the computation—that required to train the neural network—may be performed before the data packet interval measurements are made rather than after, the bottleneck link speed may be determined from the return data packet interval measurements significantly faster and using less computational resources than with statistical analysis techniques. Moreover, fewer measurements are required to determine bottleneck link speed to the same degree of accuracy.
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