A multi-task adaptive monitoring system combining different sampling primitives

Traffic measurement and analysis are crucial management activities for network operators. With the increase in traffic volume, operators resort to sampling primitives to reduce the measurement load. Unfortunately, existing systems use sampling primitives separately and configure them statically to achieve some performance objective. It becomes then important to design a new system that combines different existing sampling primitives together to support a large spectrum of monitoring tasks while providing the best possible accuracy by spatially correlating measurements and adapting the configuration to traffic variability. In this paper, and to prove the interest of the joint approach, we introduce an adaptive system that combines two sampling primitives, packet sampling and flow sampling, and that is able to satisfy multiple monitoring tasks. Our system consists of two main functions: (i) a global estimator that investigates measurements done by the different sampling primitives in order to deal with multiple monitoring tasks and to construct a more reliable global estimator while providing visibility over the entire network; (ii) an optimization method based on overhead prediction that allows to reconfigure monitors according to accuracy requirements and monitoring constraints. We present an exhaustive experimental methodology with different monitoring tasks in order to assess the performance of our system. Our experimentations are done on our MonLab platform that we developed for the purpose of this research.