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FRAPpuccino: Fault-detection through Runtime Analysis of Provenance

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2017

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Han, Xueyuan, Thomas Pasquier, Tanvi Ranjan, Mark Goldstein, and Margo Seltzer. 2017. FRAPpuccino: Fault-detection through Runtime Analysis of Provenance. In Workshop Programs of Hotcloud '17: 9th USENIX Workshop on Hot Topics in Cloud Computing, Santa Clara, CA, July 10-11, 2017.

Abstract

We present FRAPpuccino (or FRAP), a provenance- based fault detection mechanism for Platform as a Ser- vice (PaaS) users, who run many instances of an appli- cation on a large cluster of machines. FRAP models, records, and analyzes the behavior of an application and its impact on the system as a directed acyclic provenance graph. It assumes that most instances behave normally and uses their behavior to construct a model of legitimate behavior. Given a model of legitimate behavior, FRAP uses a dynamic sliding window algorithm to compare a new instance’s execution to that of the model. Any in- stance that does not conform to the model is identified as an anomaly. We present the FRAP prototype and ex- perimental results showing that it can accurately detect application anomalies.

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http://nrs.harvard.edu/urn-3:HUL.InstRepos:35350572

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