Xueyuan Han, Thomas Pasquier, Tanvi Ranjan, Mark Goldstein, and Margo Seltzer, Harvard University
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.
Open Access Media
USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.
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},
month = jul
}
