Intelligent Link Load Control in a Segment Routing network via Deep Reinforcement Learning

Aureli, Davide; Cianfrani, Antonio; Listanti, M.; Polverini, Marco

doi:10.1109/icin53892.2022.9758091

Cited by 3 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine Learning (ML) has become a widely adopted decision-support tool in communications networks. Remarkable results have been achieved in many applications, such as in traffic prediction [1], failure detection [2], traffic engineering [3], link load control [4], and routing and spectrum assignment problems in optical networks [5], [6].…”

Section: Introductionmentioning

confidence: 99%

DeepLS: Local Search for Network Optimization Based on Lightweight Deep Reinforcement Learning

Cicco

Ibrahimi

Troia

et al. 2024

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

Deep Reinforcement Learning (DRL) is being investigated as a competitive alternative to traditional techniques for solving network optimization problems. A promising research direction lies in enhancing traditional optimization algorithms by offloading low-level decisions to a DRL agent. In this study, we consider how to effectively employ DRL to improve the performance of Local Search algorithms, i.e., algorithms that, starting from a candidate solution, explore the solution space by iteratively applying local changes (i.e., moves), yielding the best solution found in the process. We propose a Local Search algorithm based on lightweight Deep Reinforcement Learning (DeepLS) that, given a neighborhood, queries a DRL agent for choosing a move, with the goal of achieving the best objective value in the long term. Our DRL agent, based on permutationequivariant neural networks, is composed by less than a hundred parameters, requiring only up to ten minutes of training and can evaluate problem instances of arbitrary size, generalizing to networks and traffic distributions unseen during training. We evaluate DeepLS on two illustrative NP-Hard network routing problems, namely OSPF Weight Setting and Routing and Wavelength Assignment, training on a single small network only and evaluating on instances 2x-10x larger than training. Experimental results show that DeepLS outperforms existing DRL-based approaches from literature and attains competitive results with state-of-the-art metaheuristics, with computing times up to 8x smaller than the strongest algorithmic baselines.

show abstract