Distributed Spectrum-Based Fault Localization

Avraham Natan; Roni Stern; Meir Kalech

doi:10.1609/aaai.v37i5.25798

Distributed Spectrum-Based Fault Localization

Avraham Natan, Roni Stern, Meir Kalech

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Spectrum-Based Fault Localization (SFL) is a popular approach for diagnosing faulty systems. SFL algorithms are inherently centralized, where observations are collected and analyzed by a single diagnoser. Applying SFL to diagnose distributed systems is challenging, especially when communication is costly and there are privacy concerns. We propose two SFL-based algorithms that are designed for distributed systems: one for diagnosing a single faulty component and one for diagnosing multiple faults. We analyze these algorithms theoretically and empirically. Our analysis shows that the distributed SFL algorithms we developed output identical diagnoses to centralized SFL while preserving privacy.

Original language	English
Title of host publication	AAAI-23 Technical Tracks 5
Editors	Brian Williams, Yiling Chen, Jennifer Neville
Publisher	AAAI press
Pages	6491-6498
Number of pages	8
ISBN (Electronic)	9781577358800
DOIs	https://doi.org/10.1609/aaai.v37i5.25798
State	Published - 27 Jun 2023
Externally published	Yes
Event	37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States Duration: 7 Feb 2023 → 14 Feb 2023

Publication series

Name	Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Volume	37

Conference

Conference	37th AAAI Conference on Artificial Intelligence, AAAI 2023
Country/Territory	United States
City	Washington
Period	7/02/23 → 14/02/23

Bibliographical note

Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Funding

This research was funded by ISF grant No. 1716/17, by the ministry of science grant No. 3-6078, and (partially) by the The Israeli Smart Transportation Research Center (ISTRC).

Funders	Funder number
Israeli Smart Transportation Research Center
Ministry of Science, ICT and Future Planning	3-6078
Israel Science Foundation	1716/17

Access to Document

10.1609/aaai.v37i5.25798

Cite this

@inproceedings{94426ba88a5247a790a468eae5c00d79,

title = "Distributed Spectrum-Based Fault Localization",

abstract = "Spectrum-Based Fault Localization (SFL) is a popular approach for diagnosing faulty systems. SFL algorithms are inherently centralized, where observations are collected and analyzed by a single diagnoser. Applying SFL to diagnose distributed systems is challenging, especially when communication is costly and there are privacy concerns. We propose two SFL-based algorithms that are designed for distributed systems: one for diagnosing a single faulty component and one for diagnosing multiple faults. We analyze these algorithms theoretically and empirically. Our analysis shows that the distributed SFL algorithms we developed output identical diagnoses to centralized SFL while preserving privacy.",

author = "Avraham Natan and Roni Stern and Meir Kalech",

note = "Publisher Copyright: Copyright {\textcopyright} 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 37th AAAI Conference on Artificial Intelligence, AAAI 2023 ; Conference date: 07-02-2023 Through 14-02-2023",

year = "2023",

month = jun,

day = "27",

doi = "10.1609/aaai.v37i5.25798",

language = "אנגלית",

series = "Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023",

publisher = "AAAI press",

pages = "6491--6498",

editor = "Brian Williams and Yiling Chen and Jennifer Neville",

booktitle = "AAAI-23 Technical Tracks 5",

}

Natan, A, Stern, R & Kalech, M 2023, Distributed Spectrum-Based Fault Localization. in B Williams, Y Chen & J Neville (eds), AAAI-23 Technical Tracks 5. Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, vol. 37, AAAI press, pp. 6491-6498, 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Washington, United States, 7/02/23. https://doi.org/10.1609/aaai.v37i5.25798

Distributed Spectrum-Based Fault Localization. / Natan, Avraham; Stern, Roni; Kalech, Meir.
AAAI-23 Technical Tracks 5. ed. / Brian Williams; Yiling Chen; Jennifer Neville. AAAI press, 2023. p. 6491-6498 (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Distributed Spectrum-Based Fault Localization

AU - Natan, Avraham

AU - Stern, Roni

AU - Kalech, Meir

PY - 2023/6/27

Y1 - 2023/6/27

N2 - Spectrum-Based Fault Localization (SFL) is a popular approach for diagnosing faulty systems. SFL algorithms are inherently centralized, where observations are collected and analyzed by a single diagnoser. Applying SFL to diagnose distributed systems is challenging, especially when communication is costly and there are privacy concerns. We propose two SFL-based algorithms that are designed for distributed systems: one for diagnosing a single faulty component and one for diagnosing multiple faults. We analyze these algorithms theoretically and empirically. Our analysis shows that the distributed SFL algorithms we developed output identical diagnoses to centralized SFL while preserving privacy.

AB - Spectrum-Based Fault Localization (SFL) is a popular approach for diagnosing faulty systems. SFL algorithms are inherently centralized, where observations are collected and analyzed by a single diagnoser. Applying SFL to diagnose distributed systems is challenging, especially when communication is costly and there are privacy concerns. We propose two SFL-based algorithms that are designed for distributed systems: one for diagnosing a single faulty component and one for diagnosing multiple faults. We analyze these algorithms theoretically and empirically. Our analysis shows that the distributed SFL algorithms we developed output identical diagnoses to centralized SFL while preserving privacy.

UR - http://www.scopus.com/inward/record.url?scp=85167867921&partnerID=8YFLogxK

U2 - 10.1609/aaai.v37i5.25798

DO - 10.1609/aaai.v37i5.25798

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85167867921

T3 - Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023

SP - 6491

EP - 6498

BT - AAAI-23 Technical Tracks 5

A2 - Williams, Brian

A2 - Chen, Yiling

A2 - Neville, Jennifer

PB - AAAI press

T2 - 37th AAAI Conference on Artificial Intelligence, AAAI 2023

Y2 - 7 February 2023 through 14 February 2023

ER -

Distributed Spectrum-Based Fault Localization

Abstract

Publication series

Conference

Bibliographical note

Funding

Access to Document

Other files and links

Fingerprint

Cite this