The high volatility of traffic patterns in IP networks calls for dynamic routing schemes allowing to adapt resource utilization to prevailing traffic. In this paper, we focus on the problem of link weight optimization in OSPF networks where the traffic is routed along shortest paths according to the link metrics. We propose an online approach to optimize OSPF weights, and thus the routing paths, adaptively as some changes are observed in the traffic. The approach relies on the estimation of traffic demands using SNMP link counts. Experimental results on both simulated and real traffic data show that the network congestion rate can be significantly reduced with respect to a static weight configuration.

This article is devoted to nonlinear single‐path routing problems, which are known to be NP‐hard even in the simplest cases. For solving these problems, we propose an algorithm inspired from Game Theory in which individual flows are allowed to independently select their path to minimize their own cost function. We design the cost function of the flows so that the resulting Nash equilibrium of the game provides an efficient approximation of the optimal solution. We establish the convergence of the algorithm and show that every optimal solution is a Nash equilibrium of the game. We also prove that if the objective function is a polynomial of degree d ≥ 1 , then the approximation ratio of the algorithm is ( 2 1 / d − 1 ) − d . Experimental results show that the algorithm provides single‐path routings with modest relative errors with respect to optimal solutions, while being several orders of magnitude faster than existing techniques. © 2016 Wiley Periodicals, Inc. NETWORKS, Vol. 69(1), 52–66 2017

After Internet routing was shown in a number of classic measurement papers to result in paths that are sub-optimal with respect to a number of metrics, routing overlays were proposed as a method for improving performance, without the need to re-engineer the underlying network. In this paper, we present SMART, a self-healing, selfoptimizing and highly scalable routing overlay, which has a number of advantages with respect to existing solutions. First, SMART can run with off-the-shelf applications and does not require any kernel modification. In addition, SMART can be widely deployed over a sizable population of routers, because it can quickly learn and efficiently track the optimal path with a limited monitoring effort. We describe the design objectives, the architecture and the implementation of SMART, as well as the online decision methods used for learning the optimal routes. Experimental results demonstrate significant improvements over native IP routing, both in terms of latency and throughput.

This paper is devoted to non-linear single path routing problems, which are known to be NP-hard even in the simplest cases. We propose a Best Response algorithm, based on Game Theory, providing single-path routings with modest relative errors with respect to optimal solutions, while being several orders of magnitude faster than existing techniques.