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High-speed network measurement under constrained programming model

Date and Time
Thursday, March 28, 2019 - 3:00pm to 4:00pm
Zoom Meeting (off campus)

NPI Zoom Seminar-Xiaoqi (Danny) Chen

Network measurement is a vital tool for network operators to diagnose outages, optimize performance, and detect attacks. Recently, the development of programmable switches has enabled us to run measurement algorithms in the network switch directly, and analyze packets up to Tbps in throughput. However, the programming model of programmable switches is extremely constrained, which restricts the types of algorithms we can run in them.
To achieve much-needed measurement goals of network operators, we design tailored algorithms to adapt to the programming constraints imposed by practical programmable switches. We first present PRECISON, which attempts to answer the Heavy-Hitter Flow Detection problem (find out  which flows sent the largest number of packets). PRECISION can accurately identify the heavy-hitter
flows using a small amount of memory, by recirculating a small number of packets probabilistically. Then, we present Snappy, which tries to solve the Heavy Flow in the Queue problem (which flows occupied a large fraction of queuing buffer). Snappy can pinpoint the bursty flows causing ephemeral long queues and potential packet loss, by maintaining multiple traffic snapshots of short time intervals. Our measurement algorithms enable network operators to perform immediate actions against these specific network flows, inhibiting congestion in real-time, while potentially improving service quality for other network flows.
Xiaoqi is a second year PhD student at Department of Computer Science, Princeton University,  advised by Prof. Jennifer Rexford. Before joining Princeton, he received his Bachelor's degree from  Institute for Interdisciplinary Information Sciences (Yao class), Tsinghua University. His research is  running network measurements in programmable switches. Interests also include data center  networking, sketches, and network science. 

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