No abstract

Communication networks create lot of interest due to their ready applicability in performance evaluation of several communication systems. In communication systems it is customary to consider that the arrivals are characterized by Poisson process. This assumption holds good if the arrivals are homogeneous and independent of time. But in many tele and satellite communication systems the arrivals are non homogeneous and the arrival rate is time dependent. Hence, in this paper we develop and analyze a three node communication network model with the assumption that the arrivals are characterized by non homogeneous Poisson process. It is further assumed that transmission time required by each packet at each node is dependent on the content of the buffer connected to it. The transient behavior of the network model is analyzed by deriving the system performance measures like mean number of packets in each buffer, mean delay in transmission, the throughput of the nodes, utilization of transmitters, etc,. The sensitivity analysis of the model reveals that the non homogeneous Poisson arrivals and dynamic bandwidth allocation strategy can reduce burstness in buffer and improve quality of service. A comparative study of communication network with non homogeneous Poisson arrivals and Poisson arrivals is also given.

In this paper, we develop a two node tandem communication network model with dynamic bandwidth allocation and feedback for the first node. In most of the communication systems, the arrivals of packets follow Non-Homogeneous and arrival rate is time dependent. In this model, the transmission rate of each transmitter depends on the number of packets in the buffer connected it. The transmission rates at each transmitter are adjusted depending upon the content of the buffer connected to it. The packets transmitted through the first transmitter may be forwarded to the buffer connected to the second transmitter or returned back to the first buffer with certain probabilities. Using the difference-differential equations the performance measures including average number of packets in each buffer, the probability of emptiness of the network, the average waiting time in the buffer and in the network, the throughput of the transmitters, and the variance of the number of packets in the buffer are calculated. It is observed that the load dependent transmission can reduce the delays in the transmission and enhance the channel capacity.

Tandem Queues are widely used in mathematical modeling of random processes describing the operation of Manufacturing systems , supply chains, Computer and telecommunication networks. In many of the communication systems the arrivals are time dependent and can be characterized by a non homogeneous Poisson process. In this paper we developed and analyzed three nodes connected in tandem Queue with feedback for the first and second nodes assuming that arrivals follow non homogeneous Poisson process. Using the difference-differential equations and a probability generating function of the number of packets in the buffer connected to the transmitter the System is analyzed. The System performance is analysed by deriving expressions for the performance measures of the network like mean content of the buffers, mean delays through put, transmitter utilization with mathematical illustrations. The sensitivity analysis of the model reveals that the non homogeneous Poisson arrivals and dynamic bandwidth allocation strategy can reduce burstness in buffer and improve quality of service.

Queuing models play a dominant role in many communication systems for optimum utilization of the resources. In this paper, we develop and analyze a two node tandem communication network model with feedback for the first node, with an assumption that the arrivals follow homogeneous Poisson process. In this model, the service rates of each transmitter depends on the number of services in the buffer connected it. The model is analyzed using the difference-differential equations and a probability generating function of the number of packets in the buffer. Expressions are derived for performance measures including average number of packets in each buffer, the probability of emptiness of the network, the mean delay in the buffer and in the network, the throughput of the transmitters, and the variance of the number of packets in the buffer.