Traffic monitoring is foundational to the network control loop, ensuring that modern networks remain secure, reliable, and performant. Fine-grained monitoring can enable powerful failure mitigation and intrusion detection, but achieving this level of visibility at scale requires either computationally intensive in-network processing or centralized solutions capable of massive data ingestion. These demands often force operators to reduce network visibility, thereby weakening control loop effectiveness. In this talk, Jonatan will introduce Direct Telemetry Access, a high-speed telemetry collection solution capable of line-rate data ingestion, and discuss his recent work on spatiotemporal sketch disaggregation — a method that delivers accurate, network-wide flow-level statistics, even in highly restrictive and heterogeneous environments.
Jonatan Langlet is a postdoctoral researcher at KTH Royal Institute of Technology, specializing in near-hardware algorithms and data structures. He earned his PhD in computer science from Queen Mary University of London, during which he worked as a Harvard research fellow. His work centers on high-speed data aggregation, probabilistic streaming analytics, and in-network artificial intelligence, with broader interests in network programmability, systems, and algorithms, emphasizing real-world deployability. His postdoctoral research is supported by a fellowship at the Digital Futures research center.