  Automatic network management driven by Artificial Intelligent technologies
has been heatedly discussed over decades. However, current reports mainly focus
on theoretic proposals and architecture designs, works on practical
implementations on real-life networks are yet to appear. This paper proposes
our effort toward the implementation of knowledge graph driven approach for
autonomic network management in software defined networks (SDNs), termed as
SeaNet. Driven by the ToCo ontology, SeaNet is reprogrammed based on Mininet (a
SDN emulator). It consists three core components, a knowledge graph generator,
a SPARQL engine, and a network management API. The knowledge graph generator
represents the knowledge in the telecommunication network management tasks into
formally represented ontology driven model. Expert experience and network
management rules can be formalized into knowledge graph and by automatically
inferenced by SPARQL engine, Network management API is able to packet
technology-specific details and expose technology-independent interfaces to
users. The Experiments are carried out to evaluate proposed work by comparing
with a commercial SDN controller Ryu implemented by the same language Python.
The evaluation results show that SeaNet is considerably faster in most
circumstances than Ryu and the SeaNet code is significantly more compact.
Benefit from RDF reasoning, SeaNet is able to achieve O(1) time complexity on
different scales of the knowledge graph while the traditional database can
achieve O(nlogn) at its best. With the developed network management API, SeaNet
enables researchers to develop semantic-intelligent applications on their own
SDNs.
