Paper Info
Title
PerfSig: Extracting Performance Bug Signatures via Multi-modality Causal Analysis
Abstract
Diagnosing a performance bug triggered in production cloud environments is notoriously challenging. Extracting performance bug signatures can help cloud operators quickly pinpoint the problem and avoid repeating manual efforts for diagnosing similar performance bugs. In this paper, we present PerfSig, a multi-modality performance bug signature extraction tool which can identify principal anomaly patterns and root cause functions for performance bugs. PerfSig performs fine-grained anomaly detection over various machine data such as system metrics, system logs, and function call traces. We then conduct causal analysis across different machine data using information theory method to pinpoint the root cause function of a performance bug. PerfSig generates bug signatures as the combination of the identified anomaly patterns and root cause functions. We have implemented a prototype of PerfSig and conducted evaluation using 20 real world performance bugs in six commonly used cloud systems. Our experimental results show that PerfSig captures various kinds of fine-grained anomaly patterns from different machine data and successfully identifies the root cause functions through multi-modality causal analysis for 19 out of 20 tested performance bugs.
Year
DOI
Venue
2022
10.1145/3510003.3510110
2022 ACM/IEEE 44TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2022)
Keywords
DocType
ISSN
Debugging, Bug signatures, Software reliability, Performance
Conference
0270-5257
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Jingzhu He100.34
Yuhang Lin200.34
Xiaohui Gu31975103.57
Chin-Chia Michael Yeh400.34
Zhongfang Zhuang500.68