Joint optimization of topology, switching, routing and wavelength assignment

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 279-285).To provide end users with economic access to high bandwidth, the architecture of the next generation metropolitan area networks (MANs) needs to be judiciously designed from the cost perspective. In addition to a low initial capital investment, the ultimate goal is to design networks that exhibit excellent scalability - a decreasing cost-per-node-per-unit-traffic as user number and transaction size increase. As an effort to achieve this goal, in this thesis we search for the scalable network architectures over the solution space that embodies the key aspects of optical networks: fiber connection topology, switching architecture selection and resource dimensioning, routing and wavelength assignment (RWA). Due to the inter-related nature of these design elements, we intended to solve the design problem jointly in the optimization process in order to achieve over-all good performance. To evaluate how the cost drives architectural tradeoffs, an analytical approach is taken in most parts of the thesis by first focusing on networks with symmetric and well defined structures (i.e., regular networks) and symmetric traffic patterns (i.e., all-to-all uniform traffic), which are fair representations that give us suggestions of trends, etc.(cont.) We starts with a examination of various measures of regular topologies. The average minimum hop distance plays a crucial role in evaluating the efficiency of network architecture. From the perspective of designing optical networks, the amount of switching resources used at nodes is proportional to the average minimum hop distance. Thus a smaller average minimum hop distance translates into a lower fraction of pass-through traffic and less switching resources required. Next, a first-order cost model is set up and an optimization problem is formulated for the purpose of characterizing the tradeoffs between fiber and switching resources. Via convex optimization techniques, the joint optimization problem is solved analytically for (static) uniform traffic and symmetric networks. Two classes of regular graphs - Generalized Moore Graphs and A-nearest Neighbors Graphs - are identified to yield lower and upper cost bounds, respectively. The investigation of the cost scalability further demonstrates the advantage of the Generalized Moore Graphs as benchmark topologies: with linear switching cost structure, the minimal normalized cost per unit traffic decreases with increasing network size for the Generalized Moore Graphs and their relatives.(cont.) In comparison, for less efficient fiber topologies (e.g., A-nearest Neighbors) and switching cost structures (e.g., quadratic cost), the minimal normalized cost per unit traffic plateaus or even increases with increasing network size. The study also reveals other attractive properties of Generalized Moore Graphs in conjunction with minimum hop routing - the aggregate network load is evenly distributed over each fiber. Thus, Generalized Moore Graphs also require the minimum number of wavelengths to support a given uniform traffic demand. Further more, the theoretical works on the Generalized Moore Graphs and their close relatives are extended to study more realistic design scenarios in two aspects. One aspect addresses the irregular topologies and (static) non-uniform traffic, for which the results of Generalized Moore networks are used to provide useful estimates of network cost, and are thus offering good references for cost-efficient optical networks. The other aspect deals with network design under random demands. Two optimization formulations that incorporate the traffic variability are presented.(cont.) The results show that as physical architecture, Generalized Moore Graphs are most robust (in cost) to the demand uncertainties. Analytical results also provided design guidelines on how optimum dimensioning, network connectivity, and network costs vary as functions of risk aversion, service level requirements, and probability distributions of demands.by Kyle Chi Guan.Ph.D