Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks

TL;DR

This paper introduces a destination-aware adaptive traffic flow rule aggregation mechanism for SDN that dynamically adjusts flow entries based on traffic monitoring needs, improving switch performance without sacrificing traffic detail.

Contribution

It presents a novel machine-learning-based adaptive aggregation method for SDN traffic flow rules, enhancing performance and traffic detail management.

Findings

Significant performance improvements in SDN switches.

Effective traffic flow detail adaptation on demand.

Machine learning enables dynamic rule aggregation.

Abstract

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.