Resource Coordination Learning for End-to-End Network Slicing Under Limited State Visibility

Network slicing is a key technological concept for next-generation networking to provide logically dedicated, customized connections for diverse use cases. In many use cases that require access to cloud facilities located far from the network edge, it is crucial to guarantee end-to-end (E2E) performance. However, the composition of E2E network slices demands complex resource coordination among multiple administrative domains that may limit the exposure of network state (e.g., topology, latency) within them for privacy and safety reasons. This paper discusses a resource coordination problem to construct E2E network slices hosted over multiple domains under limited state visibility. The resource coordination task can be formally described as a regret minimization problem with a linear objective function. We present a novel hybrid approach that incorporates partial resource information reported by each domain into the Learning with Linear Rewards (LLR) algorithm. Our experiment results show that the proposed algorithm performs significantly better than other baseline learning algorithms and the LLR algorithm, especially when the traffic patterns of network slices are more dynamic and unstable.