Mathematics of Information Technology and Complex Systems Complex Adaptive Networks for Computing and Communication (CANCCOM)


Project Highlights


Team Members

Partner Organizations







    - Self-Scheduling and High Speed Netowrks

Led by Prof. Raj Srinivasan

Modern telecommunications networks are no longer dedicated to carrying service specific traffic such as voice or data. They are expected to transport diverse sources of traffic such as voice, data, video on demand and multimedia. However, designing and managing a single network that can carry different traffic types is very complex. Sophisticated control mechanisms are required to properly guarantee Quality of Service (QoS) to all different traffic types and at the same time reduce congestion, maximize network utilization and ensure that the network is stable. Each traffic type requires different amount of network resources, namely, buffer space and bandwidth. In addition, its flow through the network is affected by network control functions such as queuing mechanisms, bandwidth allocation and routing protocols. Contrary to the best effort service provided in traditional networks, future telecommunications networks that guarantee some QoS or Service Level Agreement (SLA) to their customers should address complex tasks such Call Admission Control (CAC) and Dynamic Bandwidth Allocation (DBA) in their network management functions. Ee introduce a framework, called Self-X, for the basic network management functions. It is part of a vision of a 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 self-aspect goals, the network should possess the following. Firstly, it needs the self-knowledge, which is typically derived from accurate on-line measurements and current network conditions. Secondly, it requires the self-learning abilities to adapt to changes in traffic and network conditions.


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