Automatic belief network modeling via policy inference for SDN fault localization

Yongning Tang, Guang Cheng, Zhiwei Xu, Feng Chen, Khalid Elmansor, Yangxuan Wu
2016 Journal of Internet Services and Applications  
Fault localization for SDN becomes one of the most critical but difficult tasks. Existing tools typically only address a specific part of the problem (e.g., control plane verification, flow checker). In this paper, we propose a new approach to tackle SDN fault localization by automatically Modeling via Policy Inference (called MPI) the causality between SDN faults and their symptoms to a belief network. In the MPI system, a service oriented high level policy language is used to specify network
more » ... ervices provisioned between end nodes. MPI parses each service provisioning policy to a logical policy view, which consists of a pair of logical end nodes, a traffic pattern specification, and a list of required network functions (or a service function chain). An SDN controller takes the policies from multiple parties and provisions the requested services on its orchestrated SDN network. MPI queries the controller about the network topology and retrieves flow rules from all SDN switches. MPI maps the policy view to the corresponding implementation view, in which all the logical components in the policy view are mapped to the actual system components along with the actual network topology. Referring to the component causality graph templates derived from SDN reference model, the implementation view of the current running network services can be modeled as a belief network. A heuristic fault reasoning algorithm is adopted to search for the most likely root causes. MPI has been evaluated in both a simulation environment and a real network system for its accuracy and efficiency. The evaluation shows that MPI is a highly scalable, effective and flexible modeling approach to tackle fault localization challenges in a highly dynamic and agile SDN network.
doi:10.1186/s13174-016-0043-y fatcat:ny7oyp66vjdlrehwhs4hdpi7he