10 research outputs found
Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks
In this paper, we propose a destination-aware adaptive traffic flow rule
aggregation (DATA) mechanism for facilitating traffic flow monitoring in
SDN-based networks. This method adapts the number of flow table entries in SDN
switches according to the level of detail of traffic flow information that
other mechanisms (e.g. for traffic engineering, traffic monitoring, intrusion
detection) require. It also prevents performance degradation of the SDN
switches by keeping the number of flow table entries well below a critical
level. This level is not preset as a hard threshold but learned during
operation by using a machine-learning based algorithm. The DATA method is
implemented within a RESTful application (DATA App) which monitors and analyzes
the ongoing network traffic and provides instructions to the SDN controller to
adapt the traffic flow matching strategies accordingly. A thorough performance
evaluation of DATA is conducted in an SDN emulation environment. The results
show that---compared to the default behavior of common SDN controllers---the
proposed DATA approach yields significant SDN switch performance improvements
while still providing detailed traffic flow information on demand.Comment: This paper was presented at NetSys conference 2019. arXiv admin note:
text overlap with arXiv:1909.0154