The research community has considered in the past the application of
Artificial Intelligence (AI) techniques to control and operate networks. A
notable example is the Knowledge Plane proposed by D.Clark et al. However, such
techniques have not been extensively prototyped or deployed in the field yet.
In this paper, we explore the reasons for the lack of adoption and posit that
the rise of two recent paradigms: Software-Defined Networking (SDN) and Network
Analytics (NA), will facilitate the adoption of AI techniques in the context of
network operation and control. We describe a new paradigm that accommodates and
exploits SDN, NA and AI, and provide use cases that illustrate its
applicability and benefits. We also present simple experimental results that
support its feasibility. We refer to this new paradigm as Knowledge-Defined
Networking (KDN).Comment: 8 pages, 22 references, 6 figures and 1 tabl
The specification and enforcement of network-wide policies in a single administrative domain is common in today's networks and considered as already resolved. However, this is not the case for multi-administrative domains, e.g. among different enterprises. In such situation, new problems arise that challenge classical solutions such as PKIs, which suffer from scalability and granularity concerns. In this paper, we present an extension to Group-Based Policy -a widely used network policy language-for the aforementioned scenario. To do so, we take advantage of a permissioned blockchain implementation (Hyperledger Fabric) to distribute access control policies in a secure and auditable manner, preserving at the same time the independence of each organization. Network administrators specify polices that are rendered into blockchain transactions. A LISP control plane (RFC 6830) allows routers performing the access control to query the blockchain for authorizations. We have implemented an end-to-end experimental prototype and evaluated it in terms of scalability and network latency.
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