A Multi-task Adaptive Monitoring System Combining Different Sampling Primitives

International audienceTraffic 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

Adaptive monitoring: A systematic mapping

Context: Adaptive monitoring is a method used in a variety of domains for responding to changing conditions. It has been applied in different ways, from monitoring systems’ customization to re-composition, in different application domains. However, to the best of our knowledge, there are no studies analyzing how adaptive monitoring differs or resembles among the existing approaches. Objective: To characterize the current state of the art on adaptive monitoring, specifically to: (a) identify the main concepts in the adaptive monitoring topic; (b) determine the demographic characteristics of the studies published in this topic; (c) identify how adaptive monitoring is conducted and evaluated by the different approaches; (d) identify patterns in the approaches supporting adaptive monitoring. Method: We have conducted a systematic mapping study of adaptive monitoring approaches following recommended practices. We have applied automatic search and snowballing sampling on different sources and used rigorous selection criteria to retrieve the final set of papers. Moreover, we have used an existing qualitative analysis method for extracting relevant data from studies. Finally, we have applied data mining techniques for identifying patterns in the solutions. Results: We have evaluated 110 studies organized in 81 approaches that support adaptive monitoring. By analyzing them, we have: (1) surveyed related terms and definitions of adaptive monitoring and proposed a generic one; (2) visualized studies’ demographic data and arranged the studies into approaches; (3) characterized the main approaches’ contributions; (4) determined how approaches conduct the adaptation process and evaluate their solutions. Conclusions This cross-domain overview of the current state of the art on adaptive monitoring may be a solid and comprehensive baseline for researchers and practitioners in the field. Especially, it may help in identifying opportunities of research; for instance, the need of proposing generic and flexible software engineering solutions for supporting adaptive monitoring in a variety of systems.Peer ReviewedPostprint (author's final draft