To deliver a reliable communication service it is essential for
the network operator to manage how traffic flows in the network.
The paths taken by the traffic is controlled by the routing function.
Traditional ways of tuning routing in IP networks are designed
to be simple to manage and are not designed to adapt to the
traffic situation in the network. This can lead to congestion in
parts of the network while other parts of the network is
far from fully utilized. In this thesis we explore issues related
to optimization of the routing function to balance load in the network.
We investigate methods for efficient derivation of the
traffic situation using link count measurements. The advantage
of using link counts is that they are easily obtained and yield
a very limited amount of data. We evaluate and show that estimation
based on link counts give the operator a fast and accurate description
of the traffic demands. For the evaluation we have access to a unique data
set of complete traffic demands from an operational
IP backbone.
Furthermore, we evaluate performance of search heuristics to
set weights in link-state routing protocols. For the evaluation
we have access to complete traffic data from a Tier-1 IP network.
Our findings confirm previous studies who use partial traffic data or
synthetic traffic data. We find that optimization using estimated
traffic demands has little significance to the performance of
the load balancing.
Finally, we device an algorithm that finds a routing setting that is
robust to shifts in traffic patterns due to changes in the
interdomain routing. A set of worst case scenarios caused by the interdomain routing changes
is identified and used to solve a robust routing problem. The evaluation
indicates that performance of the robust routing is close to optimal for
a wide variety of traffic scenarios.
The main contribution of this thesis is that we demonstrate that it is
possible to estimate the traffic matrix with good accuracy and to develop
methods that optimize the routing settings to give strong and robust network
performance. Only minor changes might be necessary in order to implement our
algorithms in existing networks