Section: Research Program

Monitoring

The evolving nature of the Internet ecosystem and its continuous growth in size and heterogeneity call for a better understanding of its characteristics, limitations, and dynamics, both locally and globally so as to improve application and protocol design, detect and correct anomalous behaviors, and guarantee performance.

To face these scalability issues, appropriate monitoring models, methods and algorithms are required for data collection, analysis and sharing from which knowledge about Internet traffic and usage can be extracted. Measuring and collecting traces necessitate user-centered and data-driven paradigms to cover the wide scope of heterogeneous user activities and perceptions. In this perspective, we propose monitoring algorithms and architectures for large scale environments involving mobile and Internet of Things (IoT) devices.

Resist also assesses the impact of the Internet infrastructure evolution integrating network softwarization on monitoring, for example the need for dedicated measurement methodologies. We take into account not only the technological specifics of such paradigms for their monitoring but also the ability to use them for collecting, storing and processing monitoring data in an accurate and cost-effective manner.

Crowd-sourcing and third-party involvement are gaining in popularity, paving the way for massively distributed and collaborative monitoring. We thus investigate opportunistic mobile crowdsensing in order to collect user activity logs along with contextual information (social, demographic, professional) to effectively measure end-users' Quality of Experience. However, collaborative monitoring raises serious concerns regarding trust and sensitive data sharing (open data). Data anonymization and sanitization need to be carefully addressed.