TY - GEN
T1 - CFlam
T2 - 20th IEEE International Conference on High Performance Switching and Routing, HPSR 2019
AU - Su, Zhiyang
AU - Wang, Lu
AU - Hamdi, Mounir
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Flow latency monitoring is a fundamental task in network measurement. The development of software defined networking enables flexible flow latency monitoring in the control plane. Existing approaches mainly focus on direct probe-based latency measurement, which has a high measurement overhead, especially in high accuracy monitoring systems. In this paper, we revisit software defined latency monitoring framework and explore a cost-effective approach named CFlam to produce flow latency results. We observe large scale latency monitoring generates too many duplicate probe packets. Based on this observation, we attempt to measure only a very small subset of active flows to infer the rest flow latencies. We formulate the monitoring flow selection problem by an algebraic model, and develop an efficient algorithm to generate the optimal probe flow set. We implement and deploy CFlam on a SDN testbed to verify its feasibility and performance. We conduct experiments on a public-available network topology with real packet traces collected from a data center. Experiment results demonstrate that our scheme generates accurate flow latencies at minimum measurement overhead.
AB - Flow latency monitoring is a fundamental task in network measurement. The development of software defined networking enables flexible flow latency monitoring in the control plane. Existing approaches mainly focus on direct probe-based latency measurement, which has a high measurement overhead, especially in high accuracy monitoring systems. In this paper, we revisit software defined latency monitoring framework and explore a cost-effective approach named CFlam to produce flow latency results. We observe large scale latency monitoring generates too many duplicate probe packets. Based on this observation, we attempt to measure only a very small subset of active flows to infer the rest flow latencies. We formulate the monitoring flow selection problem by an algebraic model, and develop an efficient algorithm to generate the optimal probe flow set. We implement and deploy CFlam on a SDN testbed to verify its feasibility and performance. We conduct experiments on a public-available network topology with real packet traces collected from a data center. Experiment results demonstrate that our scheme generates accurate flow latencies at minimum measurement overhead.
UR - http://www.scopus.com/inward/record.url?scp=85071952172&partnerID=8YFLogxK
U2 - 10.1109/HPSR.2019.8808103
DO - 10.1109/HPSR.2019.8808103
M3 - Conference contribution
AN - SCOPUS:85071952172
T3 - IEEE International Conference on High Performance Switching and Routing, HPSR
BT - 2019 IEEE 20th International Conference on High Performance Switching and Routing, HPSR 2019
PB - IEEE Computer Society
Y2 - 26 May 2019 through 29 May 2019
ER -