Monte Carlo Search Algorithms for Network Traffic Engineering

Abstract

The aim of Traffic Engineering is to provide routing configurations in networks such that the used resources are minimized while maintaining a high level of quality of service (QoS). Among the optimization problems arising in this domain, we address in this paper the one related to setting weights in networks that are based on shortest path routing protocols (OSPF, IS-IS). Finding weights that induce efficient routing paths (e.g that minimize the maximum congested link) is a computationally hard problem. We propose to use Monte Carlo Search for the first time for this problem. More specifically we apply Nested Rollout Policy Adaptation (NRPA). We also extend NRPA with the force_exploration algorithm to improve the results. In comparison to other algorithms NRPA scales better with the size of the instance and can be easily extended to take into account additional constraints (cost utilization, delay, . . . ) or linear/non-linear optimization criteria. For difficult instances the optimum is not known but a lower bound can be computed. NRPA gives results close to the lower bound on a standard dataset of telecommunication networks.

Generalized Nested Rollout Policy Adaptation

“…The TSPTW is a practical problem that has everyday applications. NRPA had good results for this problem [16,21,20] as well as for the related Vehicle Routing Problem [22,12,13].…”

“…combines nested search, memorizing the best sequence of moves found at each level, and the online learning of a playout policy using this sequence. NRPA has world records in Morpion Solitaire and crossword puzzles and has also been applied to many other combinatorial problems such as the Traveling Salesman Problem with Time Windows [16,21], 3D Packing with Object Orientation [23], the physical traveling salesman problem [24], the Multiple Sequence Alignment problem [25], Logistics [22,13], Graph Coloring [14], Vehicle Routing Problems [22,12], Network Traffic Engineering [19], Virtual Network Embedding [26] or the Snake in the Box [20].…”

Joint Placement, Routing and Dimensioning at the Network Edge for Energy Minimization

“…However, in this work, the processing delays are modeled as constants, which does not take their stochastic nature into account, while it is this precise aspect which makes it hard to meet 5G's stringent SLAs. The problem of allocating resources while considering paths is also investigated by other works in the context of classic multicommodity-flow (MCF) problems [25] or OSPF weight optimization [26], [27]. In general, MCF is formulated as a resource minimization problem [28].…”

Warm-Starting Nested Rollout Policy Adaptation with Optimal Stopping

“…The problems we address were already successfully addressed using Nested Rollout Policy Adaptation (NRPA) (Rosin 2011). They are the Minimum Congestion Shortest Path Routing problem (Dang et al 2021), the Traveling Salesman Problem with Time Windows (Edelkamp et al 2013) and the Snake-in-the-Box problem (Edelkamp and Cazenave 2016). For these three problems we improve the results compared to standard NRPA.…”