PINT: Probabilistic In-band Network Telemetry
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Abstract (arXiv)
Commodity network devices support adding in-band telemetry measurements into data packets, enabling a wide range of applications, including network troubleshooting, congestion control, and path tracing. However, including such information on packets adds significant overhead that impacts both flow completion times and application-level performance. We introduce PINT, an in-band telemetry framework that bounds the amount of information added to each packet. PINT encodes the requested data on multiple packets, allowing per-packet overhead limits that can be as low as one bit. We analyze PINT and prove performance bounds, including cases when multiple queries are running simultaneously. PINT is implemented in P4 and can be deployed on network devices. Using real topologies and traffic characteristics, we show that PINT concurrently enables applications such as congestion control, path tracing, and computing tail latencies, using only sixteen bits per packet, with performance comparable to the state of the art.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jul 2020.
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Citing papers per year: 2022: 3; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
6 papers.
- 2022QUIC-FL: Quick Unbiased Compression for Federated Learningcited by 7
- 2022Impact of RoCE Congestion Control Policies on Distributed Training of DNNscited by 6
- 2022Automating In-Network Machine Learningcited by 0
- 2023GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenterscited by 0
- 2025NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load Switchescited by 0
- 2026Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetrycited by 0
- 2026Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry31 Mar 2026
- 2025NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load Switches18 Feb 2025
- 2023GraphCC: A Practical Graph Learning-based Approach to Congestion Control in Datacenters9 Aug 2023
- 2022Impact of RoCE Congestion Control Policies on Distributed Training of DNNs22 Jul 2022
- 2022QUIC-FL: Quick Unbiased Compression for Federated Learning26 May 2022
- 2022Automating In-Network Machine Learning18 May 2022