NDLI: Fault detection and localization in distributed systems using invariant relationships

Content Provider	IEEE Xplore Digital Library
Author	Sharma, A.B. Haifeng Chen Min Ding Yoshihira, K. Guofei Jiang
Copyright Year	2013
Description	Author affiliation: NEC Labs. America, Princeton, NJ, USA (Sharma, A.B.; Haifeng Chen; Min Ding; Yoshihira, K.; Guofei Jiang)
Abstract	Recent advances in sensing and communication technologies enable us to collect round-the-clock monitoring data from a wide-array of distributed systems including data centers, manufacturing plants, transportation networks, automobiles, etc. Often this data is in the form of time series collected from multiple sensors (hardware as well as software based). Previously, we developed a time-invariant relationships based approach that uses Auto-Regressive models with eXogenous input (ARX) to model this data. A tool based on our approach has been effective for fault detection and capacity planning in distributed systems. In this paper, we first describe our experience in applying this tool in real-world settings. We also discuss the challenges in fault localization that we face when using our tool, and present two approaches - a spatial approach based on invariant graphs and a temporal approach based on expected broken invariant patterns - that we developed to address this problem.
Sponsorship	IEEE Comput. Soc.
Starting Page	1
Ending Page	8
File Size	353908
Page Count	8
File Format	PDF
ISBN	9781467364713
ISSN	15300889
e-ISBN	9781467364720
DOI	10.1109/DSN.2013.6575304
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-24
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Monitoring Time measurement Noise Data models Servers
Content Type	Text
Resource Type	Article

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