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Managing Internet routers congested links with a Kohonen-RED queue
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lochin, Emmanuel Talavera, Bruno |
| Copyright Year | 2018 |
| Abstract | The behaviour of the TCP AIMD algorithm is known to cause queue length oscillations when congestion occurs at a router output link. Indeed, due to these queueing variations, end-to-end applications experience large delay jitter. Many studies have proposed efficient Active Queue Management (AQM) mechanisms in order to reduce queue oscillations and stabilize the queue length. These AQM attempt to improve the Random Early Detection (RED) model. Unfortunately, these enhancements do not react in a similar manner for various network conditions and are strongly sensitive to their initial setting parameters. Although this paper proposes a solution to overcome the difficulties of configuring the RED parameters by using a Kohonen neural network model, another goal of this study is to investigate whether cognitive intelligence could be placed in the core network to solve such stability problem. In our context, we use results from the neural network area to demonstrate that our proposal, named Kohonen-RED (KRED), enables a stable queue length without complex parameters setting or passive measurements to obtain a correct configuration. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://hal.archives-ouvertes.fr/file/index/docid/582608/filename/Lochin_4321.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Active queue management Algorithm Artificial neural network Biological Neural Networks End-to-end principle Equilibrium Erythrocytes GNU Kernel Linux Name Network congestion Network model Neural Network Simulation Neural oscillation Packet delay variation Population Parameter Random early detection Router (computing) Self-organizing map Teuvo Kohonen Throughput |
| Content Type | Text |
| Resource Type | Article |
