Nikita Ivkin, Ran Ben Basat, Zaoxing Liu, Gil Einziger, R. Friedman, V. Braverman
{"title":"I Know What You Did Last Summer: Network Monitoring using Interval Queries","authors":"Nikita Ivkin, Ran Ben Basat, Zaoxing Liu, Gil Einziger, R. Friedman, V. Braverman","doi":"10.1145/3393691.3394193","DOIUrl":null,"url":null,"abstract":"Modern telemetry systems require advanced analytic capabilities such as drill down queries. These queries can be used to detect the beginning and end of a network anomaly by efficiently refining the search space. We present the first integral solution that (i) enables multiple measurement tasks inside the same data structure, (ii) supports specifying the time frame of interest as part of its queries, and (iii) is sketch-based and thus space efficient. Namely, our approach allows the user to define both the measurement task (e.g., heavy hitters, entropy estimation, cardinality estimation) and the time frame of relevance (e.g., 5PM-6PM) at query time. Our approach provides accuracy guarantees and is the only space-efficient solution that offers such capabilities. Finally, we demonstrate how the algorithm can be used to accurately pinpoint the beginning of a realistic DDoS attack.","PeriodicalId":188517,"journal":{"name":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"115 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3393691.3394193","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
Modern telemetry systems require advanced analytic capabilities such as drill down queries. These queries can be used to detect the beginning and end of a network anomaly by efficiently refining the search space. We present the first integral solution that (i) enables multiple measurement tasks inside the same data structure, (ii) supports specifying the time frame of interest as part of its queries, and (iii) is sketch-based and thus space efficient. Namely, our approach allows the user to define both the measurement task (e.g., heavy hitters, entropy estimation, cardinality estimation) and the time frame of relevance (e.g., 5PM-6PM) at query time. Our approach provides accuracy guarantees and is the only space-efficient solution that offers such capabilities. Finally, we demonstrate how the algorithm can be used to accurately pinpoint the beginning of a realistic DDoS attack.