Network-on-Chip (NoC) is a developing and promising on-chip communication paradigm that improves scalability and performance of System-on-Chips. NoC design flow contains many problems from different areas, for example networking, embedded design and computer architecture. Application mapping is one of these problems, which is generally considered as a communication energy minimization problem. This dissertation approaches to this problem from a networking point of view and tries to find a mapping solution which improves the network performance in terms of the number of packets in the buffers while still minimizing the total communication energy consumption. For this purpose an on-chip network traffic model is required. Self similarity is a traffic model that is used to characterize Ethernet and/or wide area network traffic, as well as most of on-chip network traffic. In this thesis, by using an on-chip traffic characterization that contains self similarity, an application mapping problem definition that contains both energy and buffer utilization concerns is proposed. In order to solve this intractable problem a genetic algorithm based model is implemented. Execution of the algorithm on different test cases has proved that such a mapping formulation avoids high buffer utilizations while still keeping the communication energy low.

No / Many studies have been conducted to investigate the performance of priority queuing (PQ) systems with constant service capacity. However, due to the time-varying nature of wireless channels in wireless communication networks, the service capacity of queuing systemsmay vary over time. Therefore, it is necessary to investigate the performance of PQ systems in the presence of varying service capacity. In addition, self-similar traffic has been discovered to be a ubiquitous phenomenon in various communication networks, which poses great challenges to performance modelling of scheduling systems due to its fractal-like nature. Therefore, this paper develops a flow-decomposition based approach to performance modelling of PQ systems subject to self-similar traffic and varying service capacity. It specifically proposes an analytical model to investigate queue length distributions of individual traffic flows. The validity and accuracy of the model is demonstrated via extensive simulation experiments.

The aim of this thesis is to investigate the traffic patterns in the mobile data networks. In this work, a simple Cellular Digital Packet Data (CDPD) network was modeled in order to be used in simulations. For the purpose of using in the CDPD model, a synthetic bursty traffic model was produced and using different traffic patterns some performance investigations were made in CDPD network. During the whole work, OPNET simulation tool was used. The CDPD network modeled by OPNET simulation tool was compared with a CDPD model described in the literature and the differences were shown. The new model has some new features: 1) Burst transmission of MAC blocks. 2) Exponential backoff. 3) New packet structures. 4) Frame segmentation and encapsulation into MAC layer frames. Using OPNET, a traffic having higher level of burstiness was produced and applied to the CDPD network model. Under the bursty traffic, some CDPD performance parameters were collected and according to the collected results some suggestions were given.

No / Traffic congestion in communication networks can dramatically deteriorate user-perceived Quality-of-Service (QoS). The integration of the Random Early Detection (RED) and priority scheduling mechanisms is a promising scheme for congestion control and provisioning of differentiated QoS required by multimedia applications. Although analytical modelling of RED congestion control has received significant research efforts, the performance models reported in the current literature were primarily restricted to the RED algorithm only without consideration of traffic scheduling scheme for QoS differentiation. Moreover, for analytical tractability, these models were developed under the simplified assumption that the traffic follows Short-Range-Dependent (SRD) arrival processes (e.g., Poisson or Markov processes), which are unable to capture the self-similar nature (i.e., scale-invariant burstiness) of multimedia traffic in modern communication networks. To fill these gaps, this paper presents a new analytical model of RED congestion control for prioritized multi-class self-similar traffic. The closed-form expressions for the loss probability of individual traffic classes are derived. The effectiveness and accuracy of the model are validated through extensive comparison between analytical and simulation results. To illustrate its application, the model is adopted as a cost-effective tool to investigate the optimal threshold configuration and minimize the required buffer space with congestion control.