Project Highlights
    - Self-Scheduling in High Speed Data Networks

Modern telecommunications networks are expected to transport diverse sources of traffic such as voice, data, video on demand and multimedia. Designing and managing such a single network that can perform as well as the public switched telephone network can be very challenging. Each traffic type requires different amount of network resources (buffer space and bandwidth), and its flow through the network is affected by network control functions such as queueing mechanism, bandwidth allocation, and routing protocols. Efficient and easily implementable network management algorithms are required to properly guarantee Quality of service (QoS) to all types of traffic and at the same time reduce congestion, maximize network utilization and ensure that the network is stable. The self-x framework we introduce here for the basic network management functions is part of the vision of a self-x network that can manage its functions itself without requiring human interactions. It is expected that such a network will transport diverse sources of traffic such as voice, data and video on demand, while guaranteeing individual and specific QoS to each user. It is also expected that such a network is self-installing, self-learning, self-sizing and self-healing. In order to achieve these elf-aspect goals, the work should possess: (i) the self-knowledge which is typically derived from accurate on-line measurements and current network conditions, and (ii) the self-learning abilities to adapt to changes in traffic and network conditions. The self-x framework we propose here consists of three modules and addresses separately three functions namely, traffic prediction, traffic scheduling and connection admission control. In general, Connection Admission Control (CAC) can be defined as the procedure of deciding whether or not to accept a new connection. One of the fundamental aspects of CAC is to evaluate the impact of a new connection on the current traffic load. This usually involves determining the resources (bandwidth and buffer space) needed for a new connection with its specific (QoS) in the presence of existing connections. If the traffic generated by each connection is a deterministic function of the time, then the CAC procedure could consist of simulating off-line the traffic forwarding mechanism and in estimating the level of service provided to each. In practice, most of the time, the traffic is highly variable and can only be expected to provide its statistical characteristics. The notion of the effective bandwidth of a traffic source has been used recently to describe the effective resource requirements. This procedure is based on large deviation theory and depends strongly on the statistical properties of the traffic sources. Most of the methods require accurate statistical characteristics of the sources that may not always be feasible to obtain in practice. Besides, sources are assumed to be active indefinitely. In order to design an adaptive CAC, we introduce the notion of {\it Virtual Line Rate Required (VLRR)}, an estimator of bandwidth. For a set of sources, we use the virtual traffic, as opposed to the actual traffic. The virtual traffic is either the predicted traffic (that is calculated by the Fuzzy Traffic Predictor), or the approximated traffic. For a given mean rate $m$ and a peak rate $P$, the approximated traffic is a sequence of random numbers between $0$ and $P$, generated according to a uniform distribution such that the mean of the approximated traffic is close to $m$. The virtual traffic is then forwarded to a virtual channel using the Fuzzy Traffic Scheduler whose capacity can be fixed.