Resource Critical Flow Monitoring in Software-Defined Networks

Flow monitoring is widely applied in software-defined networks (SDNs) for monitoring network performance. Especially, detecting heavy hitters can prevent the Distributed Denial of Service (DDoS) attack. However, many existing approaches fall into one of two undesirable extremes: (i) ineffi-cient collection where only accuracy is concerned in the method; (ii) sacrifice of accuracy due to fast detection. One practical problem with this is that it does not have the flexibility to adjust the monitoring strategy to the monitoring needs, making it difficult to meet different applications. To alleviate this problem, we propose our design of a novel flow monitoring framework that keeps the balance between accuracy and efficiency. It provides customized monitoring services for applications, where network resources can be saved, and the error rate can also be confined. In this paper, we present cReFeR, a three-step compression Report-Feedback-Report framework to monitor SDNs. The IP and the value compressor are specially designed to reduce the volume of flow statistics collection. This framework thus can achieve accuracy-ensured and resource-saving flow monitoring in SDNs. Theoretical analysis and simulated evaluation have proved the effectiveness of our solution. cReFeR keeps the error rate under 3% and reduces the amount of monitoring data more than 40%, which guarantees high efficiency compared with existing methods.

Automated summary

Flow monitoring is widely used in SDNs to monitor network performance and detect heavy hitters for DDoS attack prevention. Existing approaches either focus solely on accuracy or sacrifice accuracy for fast detection, lacking flexibility to meet different monitoring needs. To address this problem, we propose a novel flow monitoring framework, cReFeR, which achieves accuracy-ensured and resource-saving monitoring in SDNs by customizing monitoring services for applications and reducing the volume of flow statistics collection.