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

Ting Dai | Zendy; Daniel J. Dean | Zendy; Peipei Wang | Zendy; Xiaohui Gu | Zendy; Shan Lu | Zendy

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

Author(s) -

Ting Dai,

Daniel J. Dean,

Peipei Wang,

Xiaohui Gu,

Shan Lu

Publication year - 2018

Publication title -

ieee transactions on parallel and distributed systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.76

H-Index - 139

eISSN - 1558-2183

pISSN - 1045-9219

DOI - 10.1109/tpds.2018.2858800

Subject(s) - computer science , cloud computing , java , software bug , production (economics) , software , code (set theory) , distributed computing , operating system , static analysis , inference , programming language , artificial intelligence , set (abstract data type) , economics , macroeconomics

Server applications running inside production cloud infrastructures are prone to various performance problems (e.g., software hang, performance slowdown). When those problems occur, developers often have little clue to diagnose those problems. In this paper, we present Hytrace, a novel hybrid approach to diagnosing performance problems in production cloud infrastructures. Hytrace combines rule-bas...

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