Providing accurate estimates of performance in large heterogeneousinternetworks, for the purposes of network design and planning andfor service provisioning has become a critical problem. This is dueto the heterogeneity of the physical medium, the size of currentand future networks and the different quality of service requirementsfor multimedia services.

In this thesis we describe our work onthe development of connection level mathematical models used forestimating network performance characteristics such as throughput,delay and blocking probability. The type of models we use andinvestigate are of the "Loss Network" type, which have been usedwidely in legacy telephone networks and in cellular networks forestimating "availability".

These network models can also be used inestimating performance in general packet-switched networks usingeffective bandwidth concepts. In particular, these models are directlyapplicable in studying connection blocking for networks using QoSrouting schemes.

Computational complexity is a serioususability bottleneck for such algorithms. We describe fast (twoto three orders of magnitude faster than discrete event simulation)approximation algorithms we have developed for accurately estimatingblocking probability in a random topology network, using state-dependentrouting, with multiple classes of traffic. We describe even fasteralgorithms based on a hierarchical loss network model we have developed.The latter are well matched to networks that have a natural hierarchicalarchitecture, or which use some form of hierarchical routing to furtherreduce computational cost. We also developed models for networks usingdelay-based QoS routing.

An important objective of our work is todemonstrate the utility of these models for effective design anddimensioning of a large network so that a certain set of QoS requirementsare met. We show how typical design and dimensioning problems can beformulated as a multi-objective constrained optimization problem, usingperformance estimation network models; an approach that leads naturallyto very useful trade-off analysis. We describe our research in thedevelopment of a general network design and dimensioning methodology bylinking our performance models with Automatic Differentiation andmulti-objective optimization algorithms and tools. We presentexamples and applications that demonstrate the speed and versatility ofour methodology and algorithms.