The efficient transportation of real-time Variable Bit Rate (VBR) video traffic in the high-speed networks has been an area of active research. The VBR video traffic characteristics having heavy tail distribution, high variance and correlation properties are quite complex to be captured by a single traffic model. While many methods have been proposed in the literature focusing on various aspects of the VBR video traffic characteristics and their impact on the traffic management, a wider perspective of various issues involved in the efficient transportation of VBR video traffic with high utilization of network resources is imperative. Moreover one important issue is the simplicity with which the bandwidth allocation and scheduling schemes can be executed online with real-time constraints in the future Gigabit networks. The correlation properties of the VBR video traffic make the predictor-based online traffic adaptation techniques attractive. In order to reduce the effects of the prediction errors on the queueing system, we have designed a novel short-term controller (STC) that works at the cell-level and the system is called the Predictor-STC system. Simple online prediction based bandwidth allocation scheme and a scheduling algorithm for the STC suitable for implementation in High-speed networks have been designed. We are developing an integrated framework addressing the issues in the VBR video traffic management based on the Predictor-STC exploiting the correlation properties.
While most of our current research relies on simulation results, the use of Gigabit Network Kit (GNK) should provide a better insight into how our architecture and other existing methods work vis-a-vis the QoS that can be provided to the end-user. We intend to evaluate the proposed architecture using the Gigabit Network Kit. More details of the research can be found at URL: http//www.eng.usf.edu/~sankar/research/p1_des.html#qos