Sketch-based measurement has emerged as a promising solutions due to its high accuracy and resource efficiency. Prior sketches focus on measuring single flow keys and cannot support measurement on multiple keys. This ...
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Sketch-based measurement has emerged as a promising solutions due to its high accuracy and resource efficiency. Prior sketches focus on measuring single flow keys and cannot support measurement on multiple keys. This work takes a significant step towards supporting arbitrarypartialkey queries, which aims to provide information for any key in the predefined range of possible flow keys. The designed system, casts arbitrarypartialkey queries to the subset sum estimation problem and makes the theoretical tools for subset sum estimation practical. utilizes two techniques: (1) stochastic variance minimization to significantly reduce per-packet update delay, and (2) removing circular dependencies in the per-packet update logic to make the implementation hardware-friendly. This paper extends the conference version by discussing how adapts to new measurement requirements, including: (1) collecting the exact information of specified flow keys, and (2) distributed measurement. is implemented on five popular platforms (CPU, Open vSwitch, Redis, P4, and FPGA). Experiment results show that compared to baselines that use traditional single-key sketches, improves average packet processing throughput by $27.2\times$ and accuracy by $10.4\times$ when measuring six flow keys.
Sketch-based measurement has emerged as a promising alternative to the traditional sampling-based network measurement approaches due to its high accuracy and resource efficiency. While there have been various designs ...
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ISBN:
(纸本)9781450383837
Sketch-based measurement has emerged as a promising alternative to the traditional sampling-based network measurement approaches due to its high accuracy and resource efficiency. While there have been various designs around sketches, they focus on measuring one particular flow key, and it is infeasible to support many keys based on these sketches. In this work, we take a significant step towards supporting arbitrarypartialkey queries, where we only need to specify a full range of possible flow keys that are of interest before measurement starts, and in query time, we can extract the information of any key in that range. We design CocoSketch, which casts arbitrarypartialkey queries to the subset sum estimation problem and makes the theoretical tools for subset sum estimation practical. To realize desirable resource-accuracy tradeoffs in software and hardware platforms, we propose two techniques: (1) stochastic variance minimization to significantly reduce per-packet update delay, and (2) removing circular dependencies in the per-packet update logic to make the implementation hardware-friendly. We implement CocoSketch on four popular platforms (CPU, Open vSwitch, P4, and FPGA) and show that compared to baselines that use traditional single-key sketches, CocoSketch improves average packet processing throughput by 27.2x and accuracy by 10.4x when measuring six flow keys.
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