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Most Influential SIGCOMM 2020 Paper · 2026-03 edition

PINT: Probabilistic In-band Network Telemetry

Ran Ben Basat, Sivaramakrishnan Ramanathan, Yuliang Li, Gianni Antichi, Minian Yu, Michael Mitzenmacher

Venue
ACM SIGCOMM Conference (SIGCOMM) 2020
Recognition
Most Influential SIGCOMM 2020 Paper (Rank No. 5)
Edition
2026-03
Impact factor
5
Certificate ID
6b05b063565b2eb2

Abstract

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 network 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.

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