ANR SAFE

Partners:

• LabHC (UJM, CNRS)
• XLIM / Univ.Poitiers
• IRISA / Univ.Rennes 1
• Huawei
• QOS DESIGN

“When applied to communication networks, traditional approaches for control and decision-making require a comprehensive knowledge of system and user behaviours, which is unrealistic in practice when there is an increase in scale and complexity. Data-driven AI approaches do not have this drawback, but offer no safety bounds and are difficult to interpret. The SAFE project aims to design an innovative approach by combining the best of both worlds. In this new approach, intelligence is distributed in the network between a global AI (at the central level) and local AIs (at the edge level) collaborating with each other by integrating traditional models with graph neural networks and reinforcement learning. The approach, developed for partially or completely observable/controllable environments, will natively integrate safety bounds, interpretability and provide self-adaptive systems for routing, traffic engineering and scheduling. SAFE has following scientific objectives with an open source strategy: 1) Hierarchical architecture: Assumingmodern network architectures, we will design a ML architecture based on global AI (running at central controller level) and local AI (running at edge device level) for decision-making in partially as well as fully observable and controllable environments. Global AI will be able to control, configure and install policies on local AI. 2) Algorithms for partially observable environments: We will design new safety bounded and interpretable algorithms for self-adaptive traffic engineering, automatic scheduling algorithms for partially observable and controllable environments. These methods find use cases in SD-WAN (Software-DefinedWide Area Networks), where edge devices present at customer premises need to collaboratively operate in overlay on top of partially observable core networks. 3) Algorithms for fully observable environments: We will investigate the application of the global and local AI architecture for fully observable and controllable environments. Specifically, we will design new safety bounded and interpretable algorithms for software-defined routing and traffic engineering, which find use cases in data centers as well as privateWANs connecting multiple sites.”