Embedded Lab. Park Yeongseong.  Introduction  Problem Formulation  Approach Overview  AOI(Area Of Interest) Extraction  CallStack Pattern Mining.

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Presentation transcript:

Embedded Lab. Park Yeongseong

 Introduction  Problem Formulation  Approach Overview  AOI(Area Of Interest) Extraction  CallStack Pattern Mining  CallStack Pattern Clustering  Evaluation  Conclusion  Q&A

 Modern software system ◦ Large number of components ◦ Large number of lines of code ◦ Depend on large number of system components  Recent Solution ◦ Microsoft Windows Error Reporting (WER) ◦ Performance bugs not handled (crash/hang debug)  PerfTrack : Event Tracing for Windows  Dtrace : Solaris and several other Unix-like system

 Propose approach ◦ StackMine  StackMine ◦ To enable performance debugging in the large ◦ Support scalable system for postmortem performance debugging  StackMine include 2phases ◦ Costly-pattern mining algorithm ◦ Clustering on the mined costly pattern

 Majority performance bug ◦ CPU consumption bug ◦ Wait bug

 3 steps to reduce the investigation scope ◦ AOI extraction ◦ Costly-maximal-pattern mining ◦ CallStack pattern clustering

 Two major issues ◦ Effectiveness ◦ Efficiency  Two effective AOI extraction techniqes ◦ Scope-based extraction ◦ Content-based extraction

 More easily recognize the common part and the variant part of the callstack patterns.  Using performance metrics of a cluster can help produce better prioritization of results for investigation

 Window Explorer UI  PerfTrack : collected 6000 trace stream  Randomly select 1000 trace stream  Relevant 921 trace stream( 181million callstack) ◦ 140 million : waiting stacks ◦ 41 running stacks

 StackMine : ranked list 1215 pattern clusters.  AOI extract : 141million -> 689thousand  Maximal-callstack-pattern mining : produce 2239 costly patterns.  Final Ranked costly pattern lists : 1215  Top 400 clusters -> 93 performance signature  58.26% of the response delay time.  Average 1.6 seconds of UI response delay

 To enable performance debugging in the large : StackMine  StackMine helps performance analysts effectively discover highly impactful performance bugs.  StackMine’s substantial benefits in performance debugging in the large