The subject of queueing theory has been developed largely in the context of telephone traffic engineering. Especially, the use of the Asynchronous Transfer Mode (ATM) technique as the transport vehicle of B-ISDN services emphasizes the importance of queueing systems to analyze traffic control schemes. In this dissertation, we present and investigate queueing models for analysis of input traffic control schemes in ATM networks.
The it Leaky Bucket (LB) scheme which regulates the input traffic to the network is one of the most promising UPC schemes for preventive congestion control. We investigate the LB scheme with a threshold in the data buffer, where leaky rate changes depending on the contents of data buffer. We use a Markov modulated Poisson process (MMPP) as a burst input traffic. We obtain the limiting distributions of the system state at an embedded point and at an arbitrary time. As performance measures we obtain loss probability and mean delay. We present some numerical results to show the effects of the level of a threshold, the rate of token generation, the size of token pool and the size of data buffer on the performances of the LB scheme with a threshold. Numerical examples show that the LB scheme with a threshold improves the system performance in comparison with the LB scheme without a threshold.
Ideal policing function must be transparent to the traffic which conforms the negotiated contracts, but it detects and drops as possible as all cells violating the contracts. Our model has almost same detection function for nonconforming traffic as the LB scheme without threshold and better performance in the cell loss probability for conforming traffic.
The LB scheme with a threshold may experience a sensitive state change around the threshold level. To improve this defect, we propose and investigate the LB schemes with two thresholds in the data buffer, in which leaky rate changes depending on the contents of data buffer. We use a Markov modulated Poisson process as a burst input traffic. We obtain the limiting distributions of the system state just after a token generation instant and at an arbitrary time. We obtain cell loss probabilities and mean cell delays for both the LB scheme with a threshold and the LB scheme with two thresholds. We also analyze these LB schemes by using Markov modulated fluid flow model. We present some numerical results to compare the performances of the LB scheme with a threshold with those of the LB scheme with two thresholds.
