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Aggregation and Degradation in JetStream: Streaming Analytics in the Wide Area

Author(s): Rabkin, Ariel; Arye, Matvey; Sen, Siddhartha; Pai, Vivek S; Freedman, Michael J

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dc.contributor.authorRabkin, Ariel-
dc.contributor.authorArye, Matvey-
dc.contributor.authorSen, Siddhartha-
dc.contributor.authorPai, Vivek S-
dc.contributor.authorFreedman, Michael J-
dc.date.accessioned2021-10-08T19:48:43Z-
dc.date.available2021-10-08T19:48:43Z-
dc.date.issued2014en_US
dc.identifier.citationRabkin, Ariel, Matvey Arye, Siddhartha Sen, Vivek S. Pai, and Michael J. Freedman. "Aggregation and Degradation in JetStream: Streaming Analytics in the Wide Area." In 11th USENIX Symposium on Networked Systems Design and Implementation (2014): pp. 275-288.en_US
dc.identifier.urihttps://www.usenix.org/system/files/conference/nsdi14/nsdi14-paper-rabkin.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1725z-
dc.description.abstractWe present JetStream, a system that allows real-time analysis of large, widely-distributed changing data sets. Traditional approaches to distributed analytics require users to specify in advance which data is to be backhauled to a central location for analysis. This is a poor match for domains where available bandwidth is scarce and it is infeasible to collect all potentially useful data. JetStream addresses bandwidth limits in two ways, both of which are explicit in the programming model. The system incorporates structured storage in the form of OLAP data cubes, so data can be stored for analysis near where it is generated. Using cubes, queries can aggregate data in ways and locations of their choosing. The system also includes adaptive filtering and other transformations that adjusts data quality to match available bandwidth. Many bandwidth-saving transformations are possible; we discuss which are appropriate for which data and how they can best be combined. We implemented a range of analytic queries on web request logs and image data. Queries could be expressed in a few lines of code. Using structured storage on source nodes conserved network bandwidth by allowing data to be collected only when needed to fulfill queries. Our adaptive control mechanisms are responsive enough to keep end-to-end latency within a few seconds, even when available bandwidth drops by a factor of two, and are flexible enough to express practical policies.en_US
dc.format.extent275 - 288en_US
dc.language.isoen_USen_US
dc.relation.ispartof11th USENIX Symposium on Networked Systems Design and Implementationen_US
dc.rightsFinal published version. This is an open access article.en_US
dc.titleAggregation and Degradation in JetStream: Streaming Analytics in the Wide Areaen_US
dc.typeConference Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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