Distributed Spectrum-Based Fault Localization

被引：0
|
作者：
Natan, Avraham [1 ]
Stern, Roni [1 ]
Kalech, Meir [1 ]
机构：
[1] Ben Gurion Univ Negev, Negev, Israel
来源：
THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 5 | 2023年
关键词：
DIAGNOSIS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：6491 / 6498
页数：8
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