SHENESYS: Load Shedding for Network Monitoring Systems

Monitoring and mining real-time network data streams is crucial for managing and operating data networks. The information that network operators desire to extract from the network traffic is of different size, granularity and accuracy depending on the measurement task (e.g., relevant data for capacity planning and intrusion detection are very different). To satisfy these different demands, a new class of monitoring systems is emerging to handle multiple arbitrary and continuous traffic queries. Such systems must cope with the effects of overload situations due to the large volumes, high data rates and bursty nature of the network traffic. This project presents the design and evaluation of a system that can shed excess load in the presence of extreme traffic conditions, while maintaining the accuracy of the traffic queries within acceptable levels. The main novelty of our approach is that it is able to operate without explicit knowledge of the traffic queries. Instead, it extracts a set of features from the traffic streams to build an on-line predictionmodel of the query resource requirements. This way the monitoring system preserves a high degree of flexibility, increasing the range of applications and network scenarios where it can be used. We implemented our scheme in an existing network monitoring system and deployed it in a research ISP network. Our results show that the system predicts the resources required to run each traffic query with errors below 5%, and that it can efficiently handle extreme load situations, preventing uncontrolled packet losses, with minimum impact on the accuracy of the queries results.

UPC, Intel

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