FRAPpuccino: Fault-detection through Runtime Analysis of Provenance

Authors: 

Xueyuan Han, Thomas Pasquier, Tanvi Ranjan, Mark Goldstein, and Margo Seltzer, Harvard University

Abstract: 

We present FRAPpuccino (or FRAP), a provenance-based fault detection mechanism for Platform as a Service (PaaS) users, who run many instances of an application 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 instance that does not conform to the model is identified as an anomaly. We present the FRAP prototype and experimental results showing that it can accurately detect application anomalies.

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BibTeX
@inproceedings {203308,
author = {Xueyuan Han and Thomas Pasquier and Tanvi Ranjan and Mark Goldstein and Margo Seltzer},
title = {FRAPpuccino: Fault-detection through Runtime Analysis of Provenance},
booktitle = {9th {USENIX} Workshop on Hot Topics in Cloud Computing (HotCloud 17)},
year = {2017},
address = {Santa Clara, CA},
url = {https://www.usenix.org/conference/hotcloud17/program/presentation/han},
publisher = {{USENIX} Association},
}