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== Abstract ==
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Network management requires accurate estimates of metrics for traffic engineering (e.g., heavy hitters), anomaly detection (e.g., entropy of source addresses), and security (e.g., DDoS detection). Obtaining accurate estimates given router CPU and memory constraints is a challenging problem. Existing approaches fall in one of two undesirable extremes: (1) low fidelity general-purpose approaches such as sampling, or (2) high fidelity but complex algorithms customized to specific application-level metrics. Ideally, a solution should be both general (i.e., supports many applications) and provide accuracy comparable to custom algorithms. This paper presents UnivMon, a framework for flow monitoring which leverages recent theoretical advances and demonstrates that it is possible to achieve both generality and high accuracy. UnivMon uses an application-agnostic data plane monitoring primitive; different (and possibly unforeseen) estimation algorithms run in the control plane, and use the statistics from the data plane to compute application-level metrics. We present a proof-of-concept implementation of UnivMon using P4 and develop simple coordination techniques to provide a ``one-big-switch'' abstraction for network-wide monitoring. We evaluate the effectiveness of UnivMon using a range of trace-driven evaluations and show that it offers comparable (and sometimes better) accuracy relative to custom sketching solutions.
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== Original document ==
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The different versions of the original document can be found in:
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* [http://dx.doi.org/10.1145/2934872.2934906 http://dx.doi.org/10.1145/2934872.2934906]
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* [https://dl.acm.org/doi/pdf/10.1145/2934872.2934906?download=true https://dl.acm.org/doi/pdf/10.1145/2934872.2934906?download=true]
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* [https://dl.acm.org/citation.cfm?id=2934906 https://dl.acm.org/citation.cfm?id=2934906],
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: [https://dblp.uni-trier.de/db/conf/sigcomm/sigcomm2016.html#LiuMVSB16 https://dblp.uni-trier.de/db/conf/sigcomm/sigcomm2016.html#LiuMVSB16],
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: [https://doi.acm.org/10.1145/2934872.2934906 https://doi.acm.org/10.1145/2934872.2934906],
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: [https://dl.acm.org/ft_gateway.cfm?id=2934906&ftid=1772107&dwn=1 https://dl.acm.org/ft_gateway.cfm?id=2934906&ftid=1772107&dwn=1],
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: [https://doi.org/10.1145/2934872.2934906 https://doi.org/10.1145/2934872.2934906],
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: [https://academic.microsoft.com/#/detail/2487095677 https://academic.microsoft.com/#/detail/2487095677]
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* [http://dl.acm.org/ft_gateway.cfm?id=2934906&ftid=1772107&dwn=1 http://dl.acm.org/ft_gateway.cfm?id=2934906&ftid=1772107&dwn=1],
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: [http://dx.doi.org/10.1145/2934872.2934906 http://dx.doi.org/10.1145/2934872.2934906] under the license http://www.acm.org/publications/policies/copyright_policy#Background
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Published on 01/01/2016

Volume 2016, 2016
DOI: 10.1145/2934872.2934906
Licence: Other

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