Hytrace: A Hybrid Approach to Performance Bug Diagnosis in Production Cloud Infrastructures

被引：4

作者：

Dai, Ting ^{[1
]}

Dean, Daniel ^{[2
]}

Wang, Peipei ^{[1
]}

Gu, Xiaohui ^{[1
]}

Lu, Shan ^{[3
]}

机构：

[1] North Carolina State Univ, Raleigh, NC 27695 USA

[2] InsightFinder Inc, Brooklyn, NY USA

[3] Univ Chicago, Chicago, IL 60637 USA

来源：

PROCEEDINGS OF THE 2017 SYMPOSIUM ON CLOUD COMPUTING (SOCC '17) | 2017年

关键词：

Hybrid analysis; performance bug diagnosis;

D O I：

10.1145/3127479.3132562

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Server applications running inside production cloud infrastructures are prone to various performance problems (e.g., software hang, performance slow down). When those problems occur, developers often have little clue to diagnose those problems. We present HyTrace, a novel hybrid approach to diagnosing performance problems in production cloud infrastructures. HyTrace combines rule-based static analysis and runtime inference techniques to achieve higher bug localization accuracy than pure-static and pure-dynamic approaches for performance bugs. HyTrace does not require source code and can be applied to both compiled and interpreted programs such as C/C++ and Java. We conduct experiments using real performance bugs from seven commonly used server applications. The results show that our approach can significantly improve the performance bug diagnosis accuracy compared to existing diagnosis techniques.

引用

页码：641 / 641

页数：1