no code implementations • 9 Aug 2023 • Guillermo Bernárdez, José Suárez-Varela, Xiang Shi, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio
The ECN configuration is thus a crucial aspect on the performance of CC protocols.
no code implementations • 11 Jul 2023 • Bin Du, He Zhang, Xiangle Cheng, Lei Zhang
The network structure reflects the edge-cloud computing topology and is trained to minimize the expectation of the cost function for unconstrained continuous optimization problems.
no code implementations • 31 Mar 2023 • Guillermo Bernárdez, José Suárez-Varela, Albert López, Xiang Shi, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio
In this paper, we present MAGNNETO, a distributed ML-based framework that leverages Multi-Agent Reinforcement Learning and Graph Neural Networks for distributed TE optimization.
2 code implementations • 22 Dec 2022 • Miquel Ferriol-Galmés, Jordi Paillisse, José Suárez-Varela, Krzysztof Rusek, Shihan Xiao, Xiang Shi, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio
We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles -- e. g., with complex non-Markovian models -- and arbitrary routing and queue scheduling configurations.
no code implementations • 28 Feb 2022 • Miquel Ferriol-Galmés, Krzysztof Rusek, José Suárez-Varela, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio
Network modeling is a fundamental tool in network research, design, and operation.
1 code implementation • 1 Feb 2022 • Carlos Güemes-Palau, Paul Almasan, Shihan Xiao, Xiangle Cheng, Xiang Shi, Pere Barlet-Ros, Albert Cabellos-Aparicio
In the context of DTN, DRL can be leveraged to solve optimization problems without directly impacting the real-world network behavior.
1 code implementation • 29 Dec 2021 • José Suárez-Varela, Paul Almasan, Miquel Ferriol-Galmés, Krzysztof Rusek, Fabien Geyer, Xiangle Cheng, Xiang Shi, Shihan Xiao, Franco Scarselli, Albert Cabellos-Aparicio, Pere Barlet-Ros
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e. g., chemistry, biology, recommendation systems).
no code implementations • 23 Dec 2021 • Xiangle Cheng, James He, Shihan Xiao, Yingxue Zhang, Zhitang Chen, Pascal Poupart, FengLin Li
Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks.
1 code implementation • 22 Sep 2021 • Paul Almasan, Shihan Xiao, Xiangle Cheng, Xiang Shi, Pere Barlet-Ros, Albert Cabellos-Aparicio
In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process.
1 code implementation • 3 Sep 2021 • Guillermo Bernárdez, José Suárez-Varela, Albert López, Bo Wu, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio
In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE.
BIG-bench Machine Learning Multi-agent Reinforcement Learning +1
no code implementations • 1 Jan 2021 • Xiangle Cheng, Yuchen He, Feifei Long, Shihan Xiao, FengLin Li
Traffic flows are the most fundamental components in a communication networking system.