Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Date
2018-08-08Preprint Posting Date
2017-05-02Online Publication Date
2018-08-08Print Publication Date
2018Permanent link to this record
http://hdl.handle.net/10754/626547
Metadata
Show full item recordAbstract
Managing 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.Citation
Gupta 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.Sponsors
We 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.Conference/Event name
2018 Conference of the ACM Special Interest Group on Data Communication, ACM SIGCOMM 2018arXiv
1705.01049Additional Links
https://dl.acm.org/citation.cfm?doid=3230543.3230555ae974a485f413a2113503eed53cd6c53
10.1145/3230543.3230555