Model-driven analytics in SDN networks
Mouli Chandramouli, Alexander Clemm · 2017
Analytics of network telemetry data is useful for addressing many important network operational problems. While Big Data techniques have been pushing scale boundaries for processing data ever further, in many cases the real bottleneck for analytics is the acquisition, i.e. the ability to generate and export the data on which analytics depends. To address this issue, we have earlier introduced DNA, a framework for Distributed Network Analytics that pushes analytics processing into the network and dynamically sets up data sources as needed. One of the challenges of such a framework concerns providing users with simple ways to articulate network analytics queries and instruct the network which data to generate and provide. We have addressed this issue using a model-driven approach that is presented in this paper. Using YANG as a way to model network analytics tasks, our system lets users articulate network analytics tasks at a very high level of abstraction that is subsequently broken down by the framework into lower-level analytics tasks which are deployed across the network.