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dc.contributor.authorGupta, Arpit
dc.contributor.authorHarrison, Rob
dc.contributor.authorCanini, Marco
dc.contributor.authorFeamster, Nick
dc.contributor.authorRexford, Jennifer
dc.contributor.authorWillinger, Walter
dc.date.accessioned2018-12-05T12:09:05Z
dc.date.available2017-12-28T07:32:15Z
dc.date.available2018-12-05T12:09:05Z
dc.date.issued2018-08-08
dc.identifier.citationGupta A, Harrison R, Canini M, Feamster N, Rexford J, et al. (2018) Sonata. Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication - SIGCOMM ’18. Available: http://dx.doi.org/10.1145/3230543.3230555.
dc.identifier.doi10.1145/3230543.3230555
dc.identifier.urihttp://hdl.handle.net/10754/626547
dc.description.abstractManaging and securing networks requires collecting and analyzing network traffic data in real time. Existing telemetry systems do not allow operators to express the range of queries needed to perform management or scale to large traffic volumes and rates. We present Sonata, an expressive and scalable telemetry system that coordinates joint collection and analysis of network traffic. Sonata provides a declarative interface to express queries for a wide range of common telemetry tasks; to enable real-time execution, Sonata partitions each query across the stream processor and the data plane, running as much of the query as it can on the network switch, at line rate. To optimize the use of limited switch memory, Sonata dynamically refines each query to ensure that available resources focus only on traffic that satisfies the query. Our evaluation shows that Sonata can support a wide range of telemetry tasks while reducing the workload for the stream processor by as much as seven orders of magnitude compared to existing telemetry systems.
dc.description.sponsorshipWe thank our shepherd (Ion Stoica), Rüdiger Birkner, Ankita Pawar, Mina T. Arashloo, Robert MacDavid, Chris Mac-Stoker, Rachit Agarwal, and the anonymous reviewers for the feedback and comments. This research was supported by NSF Awards CNS-1539902 and CNS-1704077. Jennifer Rexford was additionally supported by gifts from Intel and Huawei.
dc.publisherACM Press
dc.relation.urlhttps://dl.acm.org/citation.cfm?doid=3230543.3230555
dc.rightsArchived with thanks to Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication - SIGCOMM '18
dc.subjectAnalytics
dc.subjectProgrammable switches
dc.subjectStream processing
dc.titleSonata: query-driven streaming network telemetry
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalProceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication - SIGCOMM '18
dc.conference.date2018-08-20 to 2018-08-25
dc.conference.name2018 Conference of the ACM Special Interest Group on Data Communication, ACM SIGCOMM 2018
dc.conference.locationBudapest, HUN
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionPrinceton University
dc.contributor.institutionNIKSUN Inc.
dc.identifier.arxividarXiv:1705.01049
kaust.personCanini, Marco
refterms.dateFOA2018-06-14T09:29:46Z


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