Online Learning for Autonomous Management of Intent-Based 6G Networks

The growing complexity of networks and the variety of future scenarios with diverse and often stringent performance requirements call for a higher level of automation. Intent-based management emerges as a solution to attain high level of automation, enabling human operators to solely communicate with the network through high-level intents. The intents consist of the targets in the form of expectations (i.e., latency expectation) from a service and based on the expectations the required network configurations should be done accordingly. It is almost inevitable that when a network action is taken to fulfill one intent, it can cause negative impacts on the performance of another intent, which results in a conflict. In this paper, we address the challenge of conflict resolution in intent-based networking and propose an online learning approach based on the hierarchical multi-armed bandit framework for autonomous network management. The hierarchical structure enables efficient exploration and exploitation of network configurations while adapting to dynamic network conditions. Our proposed hierarchical multi-armed bandit conflict resolution (MABCR) approach optimizes resource allocation within a partially known system with limited bandwidth. In comparison to other approaches, we show that our algorithm is an effective approach regarding resource allocation and satisfaction of intent expectations.