Name
Abstract | ||
|---|---|---|
Efficient network management requires collecting a variety of statistics over the packet flows. Monitoring the flows directly in the data plane allows the system to detect anomalies faster. However, monitoring algorithms have to handle a throughput of 109 packets per second and to maintain a very low memory footprint. Widely adopted sampling-based approaches suffer from low accuracy in estimations. Thus, it is natural to ask: "Is it possible to maintain important statistics in the data plane using small memory footprint?". In this paper, we answer this question in affirmative for an important case of quantiles. We introduce QPipe, the first quantiles sketching algorithm that can be implemented entirely in the data plane. Our main technical contribution is an on-the-plane implementation of a variant of SweepKLL [27] algorithm. Specifically, we give novel implementations of argmin(), the major building block of SweepKLL which are usually not supported in the data plane of the commodity switch. We prototype QPipe in P4 and compare its performance with a sampling-based baseline. Our evaluations demonstrate 10× memory reduction for a fixed approximation error and 90× error improvement for a fixed amount of memory. We conclude that QPipe can be an attractive alternative to sampling-based methods.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3359989.3365433 | CoNEXT |
Keywords | Field | DocType |
Quantile, Sketch, Data plane, Programmable networks, Network monitoring | Forwarding plane,Computer science,Network packet,Algorithm,Quantile,Sampling (statistics),Throughput,Network monitoring,Memory footprint,Approximation error,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-4503-6998-5 | 6 | 0.42 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikita Ivkin | 1 | 26 | 3.90 |
| Zhuolong Yu | 2 | 25 | 3.18 |
| Vladimir Braverman | 3 | 357 | 34.36 |
| Xin Jin | 4 | 593 | 35.22 |