TY - GEN
T1 - NitroSketch
T2 - 2019 ACM Special Interest Group on Data Communication, SIGCOMM 2019
AU - Liu, Zaoxing
AU - Ben-Basat, Ran
AU - Einziger, Gil
AU - Kassner, Yaron
AU - Braverman, Vladimir
AU - Friedman, Roy
AU - Sekar, Vyas
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/8/19
Y1 - 2019/8/19
N2 - Software switches are emerging as a vital measurement vantage point in many networked systems. Sketching algorithms or sketches, provide high-fidelity approximate measurements, and appear as a promising alternative to traditional approaches such as packet sampling. However, sketches incur significant computation overhead in software switches. Existing efforts in implementing sketches in virtual switches make sacrifices on one or more of the following dimensions: performance (handling 40 Gbps line-rate packet throughput with low CPU footprint), robustness (accuracy guarantees across diverse workloads), and generality (supporting various measurement tasks). In this work, we present the design and implementation of NitroSketch, a sketching framework that systematically addresses the performance bottlenecks of sketches without sacrificing robustness and generality. Our key contribution is the careful synthesis of rigorous, yet practical solutions to reduce the number of per-packet CPU and memory operations. We implement NitroSketch on three popular software platforms (Open vSwitch-DPDK, FD.io-VPP, and BESS) and evaluate the performance. We show that accuracy is comparable to unmodified sketches while attaining up to two orders of magnitude speedup, and up to 45% reduction in CPU usage.
AB - Software switches are emerging as a vital measurement vantage point in many networked systems. Sketching algorithms or sketches, provide high-fidelity approximate measurements, and appear as a promising alternative to traditional approaches such as packet sampling. However, sketches incur significant computation overhead in software switches. Existing efforts in implementing sketches in virtual switches make sacrifices on one or more of the following dimensions: performance (handling 40 Gbps line-rate packet throughput with low CPU footprint), robustness (accuracy guarantees across diverse workloads), and generality (supporting various measurement tasks). In this work, we present the design and implementation of NitroSketch, a sketching framework that systematically addresses the performance bottlenecks of sketches without sacrificing robustness and generality. Our key contribution is the careful synthesis of rigorous, yet practical solutions to reduce the number of per-packet CPU and memory operations. We implement NitroSketch on three popular software platforms (Open vSwitch-DPDK, FD.io-VPP, and BESS) and evaluate the performance. We show that accuracy is comparable to unmodified sketches while attaining up to two orders of magnitude speedup, and up to 45% reduction in CPU usage.
KW - Flow Monitoring
KW - Sketch
KW - Sketching Algorithm
KW - Software Switch
KW - Virtual Switch
UR - https://www.scopus.com/pages/publications/85071935014
U2 - 10.1145/3341302.3342076
DO - 10.1145/3341302.3342076
M3 - Conference contribution
AN - SCOPUS:85071935014
T3 - SIGCOMM 2019 - Proceedings of the 2019 Conference of the ACM Special Interest Group on Data Communication
SP - 334
EP - 350
BT - SIGCOMM 2019 - Proceedings of the 2019 Conference of the ACM Special Interest Group on Data Communication
PB - Association for Computing Machinery
Y2 - 19 August 2019 through 23 August 2019
ER -