Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/116460
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Type: Journal article
Title: Adaptive performance anomaly detection in distributed systems using online SVMs
Author: Alvarez Cid-Fuentes, J.
Szabo, C.
Falkner, K.
Citation: IEEE Transactions on Dependable and Secure Computing, 2020; 17(5):9281-941
Publisher: IEEE
Issue Date: 2020
ISSN: 1545-5971
1941-0018
Statement of
Responsibility: 
Javier Alvarez Cid-Fuentes, Claudia Szabo, Katrina Falkner
Abstract: Performance anomaly detection is crucial for long running, large scale distributed systems. However, existing works focus on the detection of specific types of anomalies, rely on historical failure data, and cannot adapt to changes in system behavior at run time. In this work, we propose an adaptive framework for the detection and identification of complex anomalous behaviors, such as deadlocks and livelocks, in distributed systems without historical failure data. Our framework employs a two-step process involving two online SVM classifiers on periodically collected system metrics to identify at run time normal and anomalous behaviors such as deadlock, livelock, unwanted synchronization, and memory leaks. Our approach achieves over 0.70 F-score in detecting previously unseen anomalies and 0.78 F-score in identifying the type of known anomalies with a short delay after the anomalies appear, and with minimal expert intervention. Our experimental analysis uses system execution traces from our in-house distributed system with varied behaviors and a dataset by Yahoo!, and shows the benefits of our approach as well as future research challenges.
Keywords: Measurement; anomaly detection; detectors; correlation; cloud computing; system analysis and design; adaptation models
Rights: © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030084916
DOI: 10.1109/TDSC.2018.2821693
Appears in Collections:Computer Science publications

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