TY - GEN
T1 - Fine-grained queue measurement in the data plane
AU - Chen, Xiaoqi
AU - Feibish, Shir Landau
AU - Koral, Yaron
AU - Rexford, Jennifer
AU - Rottenstreich, Ori
AU - Monetti, Steven A.
AU - Wang, Tzuu Yi
PY - 2019/12/3
Y1 - 2019/12/3
N2 - Short-lived surges in traffic can cause periods of high queue utilization, leading to packet loss and delay. To diagnose and alleviate performance problems, networks need support for real-time, fine-grained queue measurement. By identifying the flows that contribute significantly to queue build-up directly in the data plane, switches can make targeted decisions to mark, drop, or reroute these flows in real time. However, collecting fine-grained queue statistics is challenging even with modern programmable switch hardware, due to limited memory and processing resources in the data plane. We present ConQuest, a compact data structure that identifies the flows making a significant contribution to the queue. ConQuest operates entirely in the data plane, while working within the hardware constraints of programmable switches. Additionally, we show how to measure queues in legacy devices through link tapping and an off-path switch running ConQuest. Simulations show that ConQuest can identify contributing flows with 90% precision on a 1 ms timescale, using less than 65 KB of memory. Experiments with our Barefoot Tofino prototype show that ConQuest-enabled active queue management reduces flow-completion time.
AB - Short-lived surges in traffic can cause periods of high queue utilization, leading to packet loss and delay. To diagnose and alleviate performance problems, networks need support for real-time, fine-grained queue measurement. By identifying the flows that contribute significantly to queue build-up directly in the data plane, switches can make targeted decisions to mark, drop, or reroute these flows in real time. However, collecting fine-grained queue statistics is challenging even with modern programmable switch hardware, due to limited memory and processing resources in the data plane. We present ConQuest, a compact data structure that identifies the flows making a significant contribution to the queue. ConQuest operates entirely in the data plane, while working within the hardware constraints of programmable switches. Additionally, we show how to measure queues in legacy devices through link tapping and an off-path switch running ConQuest. Simulations show that ConQuest can identify contributing flows with 90% precision on a 1 ms timescale, using less than 65 KB of memory. Experiments with our Barefoot Tofino prototype show that ConQuest-enabled active queue management reduces flow-completion time.
KW - Network monitoring
KW - P4
KW - Queue measurement
KW - SDN
UR - http://www.scopus.com/inward/record.url?scp=85077229912&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077229912&partnerID=8YFLogxK
U2 - 10.1145/3359989.3365408
DO - 10.1145/3359989.3365408
M3 - Conference contribution
T3 - CoNEXT 2019 - Proceedings of the 15th International Conference on Emerging Networking Experiments and Technologies
SP - 15
EP - 29
BT - CoNEXT 2019 - Proceedings of the 15th International Conference on Emerging Networking Experiments and Technologies
PB - Association for Computing Machinery, Inc
T2 - 15th ACM International Conference on Emerging Networking Experiments and Technologies, CoNEXT 2019
Y2 - 9 December 2019 through 12 December 2019
ER -