Towards a Cognitive Routing Engine for Software Defined Networks
This addresses the issue of high monitoring overhead and slow response times in SDN controllers and switches, offering a domain-specific improvement for network traffic engineering.
The paper tackles the problem of excessive monitoring in Software Defined Networks (SDN) for traffic engineering by proposing a Cognitive Routing Engine (CRE) that finds near-optimal QoS paths with significantly reduced monitoring overhead, achieving a 1.65% optimality gap and 9.5 times less monitoring in an evaluation on the GEANT network.
Most Software Defined Networks (SDN) traffic engineering applications use excessive and frequent global monitoring in order to find the optimal Quality-of-Service (QoS) paths for the current state of the network. In this work, we present the motivations, architecture and initial evaluation of a SDN application called Cognitive Routing Engine (CRE) which is able to find near-optimal paths for a user-specified QoS while using a very small monitoring overhead compared to global monitoring which is required to guarantee that optimal paths are found. Smaller monitoring overheads bring the advantage of smaller response time for the SDN controllers and switches. The initial evaluation of CRE on a SDN representation of the GEANT academic network shows that it is possible to find near-optimal paths with a small optimality gap of 1.65% while using 9.5 times less monitoring.