ATM (Asynchronous Transfer Mode) networks were deemed the best choice for multimedia communication. The traditional mode was replaced because ATM can provide varied traffic types and QoS (quality of service). Maintaining QoS, however, requires a flexible traffic control, including call admission control and congestion control. Traditional approaches fail to estimate the required bandwidth and cell loss rate precisely. To alleviate these problems, we employ AI methods to improve the capability of estimated bandwidth and predicted cell loss rate. This thesis aims to apply neural network techniques to ATM traffic control and consists of two parts. The first part concerns a neural-based call admission control, while the second part presents an intelligent congestion control for ATM networks.
In the first part, we focus on the improvement of RBF (Radial basis function) networks and the design of a neural-based call admission control. RBF networks have been widely used for modeling a function from given input-output patterns. However, two difficulties are encountered with traditional RBF networks. One is that the initial configuration of a RBF network needs to be determined by a trial-and-error method. The other is that the performance suffers from some difficulties when the desired output has abrupt changes or constant values in certain intervals. We propose a novel approach to overcome these difficulties. New kernel functions are used for hidden nodes, and the number of nodes is determined automatically by an ART-like algorithm. Parameters and weights are initialized appropriately, and then tuned and adjusted by the gradient descent method to improve the performance of the network. Then, we employ ART-RBF networks to design and implement a call admission control. Traditional approaches fail to estimate appropriately the required bandwidth, leading to a waste of bandwidth or a high cell loss rate. To alleviate the problem, we employ ART-RBF networks to estimate the required bandwidth, and thus a new connection request can then be accepted or rejected. Because of the more accurate estimation on the required bandwidth, the proposed method can provide a better control on quality of service for ATM networks.
In the second part, we propose a neural-fuzzy rate-based feedback congestion control for ATM networks. Traditional methods perform congestion control by monitoring the queue length. The source rate is decreased by a fixed rate when the queue length is greater than a predefined threshold. However, it is difficult to get a suitable rate according to the degree of traffic congestion. We employ a neural-fuzzy mechanism to control the source rate. Through learning, cell loss can be predicted from the current value and the derivative of the queue length. Then an explicit rate is calculated and the source rate is controlled appropriately.
In summary, we have proposed improvements on architecture and performance of neural networks, and applied neural networks to traffic control for ATM networks. We have developed some control mechanisms which, through simulations, have been shown to be more effective than traditional methods.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0211103-144433 |
| Date | 11 February 2003 |
| Creators | Hou, Chun-Liang |
| Contributors | Cheng-Seen Ho, Chua-Chin Wang, Pau-Choo Chung, Shie-Jue Lee, Tzung-Pei Hong, Chin-Teng Lin |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0211103-144433 |
| Rights | campus_withheld, Copyright information available at source archive |
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