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Instantiating the Knowledge Plane
arXiv:2611.07827v1 Announce Type: cross Abstract: In 2003, Clark et al. proposed the "Knowledge Plane", a layer of intelligent agents that share rich information about network state in order to collaboratively manage the Internet across administrative boundaries. The technology of the day lacked the two capabilities it demanded most: reasoning and unstructured communication. Thus, automated network management has remained largely confined to single domains, while interdomain problems are still resolved by human operators on the phone. We argue that agentic large language models can finally unlock the potential of the Knowledge Plane, but that doing so will require solving new research problems that arise particularly in this decentralized, multi-agent, coopetitive setting that have received limited attention in the literature thus far.
Abstract:In 2003, Clark et al. proposed the "Knowledge Plane", a layer of intelligent agents that share rich information about network state in order to collaboratively manage the Internet across administrative boundaries. The technology of the day lacked the two capabilities it demanded most: reasoning and unstructured communication. Thus, automated network management has remained largely confined to single domains, while interdomain problems are still resolved by human operators on the phone. We argue that agentic large language models can finally unlock the potential of the Knowledge Plane, but that doing so will require solving new research problems that arise particularly in this decentralized, multi-agent, coopetitive setting that have received limited attention in the literature thus far.
Accepted at HotNets '26 (Salt Lake City, UT, USA). 11 pages
Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
arXiv:2611.07827 [cs.NI]
(or arXiv:2611.07827v1 [cs.NI] for this version)
https://doi.org/10.48550/arXiv.2611.07827 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1145/3847290.3859939 Focus to learn more DOI(s) linking to related resources
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