TY - GEN
T1 - Heavy-hitter detection entirely in the data plane
AU - Sivaraman, Vibhaalakshmi
AU - Narayana, Srinivas
AU - Rottenstreich, Ori
AU - Muthukrishnan, S.
AU - Rexford, Jennifer L.
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/4/3
Y1 - 2017/4/3
N2 - Identifying the "heavy hitter" flows or flows with large traffic volumes in the data plane is important for several applications e.g., flow-size aware routing, DoS detection, and traffic engineering. However, measurement in the data plane is constrained by the need for linerate processing (at 10-100Gb/s) and limited memory in switching hardware. We propose HashPipe, a heavy hitter detection algorithm using emerging programmable data planes. HashPipe implements a pipeline of hash tables which retain counters for heavy flows while evicting lighter flows over time. We prototype HashPipe in P4 and evaluate it with packet traces from an ISP backbone link and a data center. On the ISP trace (which contains over 400,000 flows), we find that HashPipe identifies 95% of the 300 heaviest flows with less than 80KB of memory.
AB - Identifying the "heavy hitter" flows or flows with large traffic volumes in the data plane is important for several applications e.g., flow-size aware routing, DoS detection, and traffic engineering. However, measurement in the data plane is constrained by the need for linerate processing (at 10-100Gb/s) and limited memory in switching hardware. We propose HashPipe, a heavy hitter detection algorithm using emerging programmable data planes. HashPipe implements a pipeline of hash tables which retain counters for heavy flows while evicting lighter flows over time. We prototype HashPipe in P4 and evaluate it with packet traces from an ISP backbone link and a data center. On the ISP trace (which contains over 400,000 flows), we find that HashPipe identifies 95% of the 300 heaviest flows with less than 80KB of memory.
KW - Network algorithms
KW - Network monitoring
KW - Programmable networks
KW - Software-defined networks
UR - http://www.scopus.com/inward/record.url?scp=85018957154&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018957154&partnerID=8YFLogxK
U2 - 10.1145/3050220.3063772
DO - 10.1145/3050220.3063772
M3 - Conference contribution
AN - SCOPUS:85018957154
T3 - SOSR 2017 - Proceedings of the 2017 Symposium on SDN Research
SP - 164
EP - 176
BT - SOSR 2017 - Proceedings of the 2017 Symposium on SDN Research
PB - Association for Computing Machinery, Inc
T2 - 2017 Symposium on SDN Research, SOSR 2017
Y2 - 3 April 2017 through 4 April 2017
ER -