Research Area: Software Defined Networks
In this thesis, a novel framework for dynamic congestion control has been proposed. The study is about the congestion control in broadband communication networks. Congestion results when demand temporarily exceeds capacity and leads to severe degradation of Quality of Service (QoS) and possibly loss of traffic. Since traffic is stochastic in nature, high demand may arise anywhere in a network and possibly causing congestion. There are different ways to mitigate the effects of congestion, by rerouting, by aggregation to take advantage of statistical multiplexing, and by discarding too demanding traffic, which is known as admission control.
This thesis will try to accommodate as much traffic as possible, and study the effect of routing and aggregation on a rather general mix of traffic types. Software Defined Networking (SDN) and Network Function Virtualization (NFV) are concepts that allow for dynamic configuration of network resources by decoupling control from payload data and allocation of network functions to the most suitable physical node. This allows implementation of a centralized control that takes the state of the entire network into account and configures nodes dynamically to avoid congestion. Assumes that node controls can be expressed in commands supported by Open Flow v1.3. Due to state dependencies in space and time, the network dynamics are very complex, and resort to a simulation approach.
The load in the network depends on many factors, such as traffic characteristics and the traffic matrix, topology and node capacities. To be able to study the impact of control functions, some parts of the environment is fixed, such as the topology and the node capacities, and statistically average the traffic distribution in the network by randomly generated traffic matrices. The traffic consists of approximately equal intensity of smooth, burst and long memory traffic. By designing an algorithm that route traffic and configure queue resources so that delay is minimized, this thesis chooses the delay to be the optimization parameter because it is additive and real-time applications are delay sensitive. The optimization being studied both with respect to total end-to-end delay and maximum end-to-end delay.
Name of the Researcher: Kamaruddin, Amalina Farhan
Name of the Supervisor(s): Al-Raweshidy, H
Year of Completion: 2017
University: Brunel University London
Thesis Link: Home Page Url
