Section: New Results
Adaptive multi-path routing
Routing plays a crucial part in the efficient operation of packet-switched data networks, especially with regard to latency reduction and energy efficiency. However, in addition to being distributed (so as to cope with the prolific size of today's networks), optimized routing schemes must also be able to adapt to changes in the underlying network (e.g. due to variations in traffic demands, link quality, etc.).
First, to address the issue of latency reduction, we provided in  an adaptive multi-flow routing algorithm to select end-to-end paths in packet-switched networks. The algorithm is based only on local information, so it is suitable for distributed implementation; furthermore, it provides guarantees that the network configuration converges to a stable state and exhibits several robustness properties that make it suitable for use in dynamic real-life networks (such as robustness to measurement errors, outdated information and update desynchronization).
Concerning energy efficiency,  examines the problem of routing in optical networks with the aim of minimizing traffic-driven power consumption. To tackle this,  proposed a pricing scheme which, combined with a distributed learning method based on the Boltzmann distribution of statistical mechanics, exhibits remarkable operation properties even under uncertainty. Specifically, the long-term average of the network's power consumption converges quickly to its minimum value (in practice, within a few iterations of the algorithm), and this convergence remains robust in the face of uncertainty of arbitrarily high magnitude.