Resource allocation and scalability in dynamic wavelength-routed optical networks.

This thesis investigates the potential benefits of dynamic operation of wavelength-routed optical networks (WRONs) compared to the static approach. It is widely believed that dynamic operation of WRONs would overcome the inefficiencies of the static allocation in improving resource use. By rapidly allocating resources only when and where required, dynamic networks could potentially provide the same service that static networks but at decreased cost, very attractive to network operators. This hypothesis, however, has not been verified. It is therefore the focus of this thesis to investigate whether dynamic operation of WRONs can save significant number of wavelengths compared to the static approach whilst maintaining acceptable levels of delay and scalability. Firstly, the wavelength-routed optical-burst-switching (WR-OBS) network architecture is selected as the dynamic architecture to be studied, due to its feasibility of implementation and its improved network performance. Then, the wavelength requirements of dynamic WR-OBS are evaluated by means of novel analysis and simulation and compared to that of static networks for uniform and non-uniform traffic demand. It is shown that dynamic WR-OBS saves wavelengths with respect to the static approach only at low loads and especially for sparsely connected networks and that wavelength conversion is a key capability to significantly increase the benefits of dynamic operation. The mean delay introduced by dynamic operation of WR-OBS is then assessed. The results show that the extra delay is not significant as to violate end-to-end limits of time-sensitive applications. Finally, the limiting scalability of WR-OBS as a function of the lightpath allocation algorithm computational complexity is studied. The trade-off between the request processing time and blocking probability is investigated and a new low-blocking and scalable lightpath allocation algorithm which improves the mentioned trade-off is proposed. The presented algorithms and results can be used in the analysis and design of dynamic WRONs

Risk-based Survivable Network Design

Communication networks are part of the critical infrastructure upon which society and the economy depends; therefore it is crucial for communication networks to survive failures and physical attacks to provide critical services. Survivability techniques are deployed to ensure the functionality of communication networks in the face of failures. The basic approach for designing survivable networks is that given a survivability technique (e.g., link protection, or path protection) the network is designed to survive a set of predefined failures (e.g., all single-link failures) with minimum cost. However, a hidden assumption in this design approach is that the sufficient monetary funds are available to protect all predefined failures, which might not be the case in practice as network operators may have a limited budget for improving network survivability. To overcome this limitation, this dissertation proposed a new approach for designing survivable networks, namely; risk-based survivable network design, which integrates risk analysis techniques into an incremental network design procedure with budget constraints. In the risk-based design approach, the basic design problem considered is that given a working network and a fixed budget, how best to allocate the budget for deploying a survivability technique in different parts of the network based on the risk. The term risk measures two related quantities: the likelihood of failure or attack, and the amount of damage caused by the failure or attack. Various designs with different risk-based design objectives are considered, for example, minimizing the expected damage, minimizing the maximum damage, and minimizing a measure of the variability of damage that could occur in the network. In this dissertation, a design methodology for the proposed risk-based survivable network design approach is presented. The design problems are formulated as Integer Programming (InP) models; and in order to scale the solution of models, some greedy heuristic solution algorithms are developed. Numerical results and analysis illustrating different risk-based designs are presented