Abstract
With the increasing complexity of network topologies and architectures, adding intelligence to the network control plane through Artificial Intelligence and Machine Learning (AI&ML) is becoming a trend in network development. For large-scale geo-distributed systems, determining how to appropriately introduce intelligence in networking is the key to high-efficiency operation. In this treatise, we explore two deployment paradigms (centralized vs. distributed) for AI-based networking. To achieve the best results, we propose a hybrid ML paradigm that combines a distributed intelligence, based on units called "AI routers," with a centralized intelligence, called the "network mind", to support different network services. In the proposed paradigm, we deploy centralized AI control for connection-oriented tunneling-based routing protocols (such as multiprotocol label switching and segment routing) to guarantee a high QoS, whereas for hop-by-hop IP routing, we shift the intelligent control responsibility to each AI router to ease the overhead imposed by centralized control and use the network mind to improve the global convergence.
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