This paper introduces a forked communication network model with non-homogenous network model with bulk arrivals and phase type transmission. In this model it is assumed that the messages are converted into packets of random size and stored in buffers for forward transmission. | Journal of Computer Science and Information Technology December 2018, Vol. 6, No. 2, pp. 60-83 ISSN 2334-2366(Print) 2334-2374(Online) Copyright © The Author(s). All Rights Reserved. Published by American Research Institute for Policy Development DOI: URL: Forked Communication Network Model with Non-Homogenous Bulk Arrivals and Phase Type Transmission K. Srinivasa Rao1, SK. Meeravali2 & P. Srinivasa Rao3 Abstract This paper introduces a forked communication network model with non-homogenous network model with bulk arrivals and phase type transmission. In this model it is assumed that the messages are converted into packets of random size and stored in buffers for forward transmission. The arrival of messages to the network follows Poisson process and the number of packets a message can be converted is random and follows a probability distribution. It is further assumed that the arrivals of packets are time dependent. As a result of it the arrival process follows a non-homogenous compound Poisson process. This type of scenario is visible at places like MAN, WAN and LAN. It is assumed that after completing transmission of the packet from the first node it may join either of the two buffers connected in tandem to the first node with certain probabilities or the packet may leave the network. It is also assumed that the transmission processes in three nodes follow Poisson processes. The transmission rate is adjusted on the content of the buffers connected to them. The performance of the network is analysed through obtaining the explicit expressions for the performance measures such as mean number of packets in the buffer, mean delay in transmission, throughput of the nodes, and variability of the content in the buffers. The sensitivity analysis of the model with respective to the changes in the parameters is also studied. It is observed that the performance measures are highly influenced by batch size distribution
