Artemis Logs Database View Data Collectio n GUI Dryad Overview Data collection Distributed system Plug-ins GUI Plug-ins Hunting for Bugs with Artemis System Architecture Conclusio ns

pptPlex Section Divider Hunting for Bugs with Artemis The slides after this divider will be grouped into a section and given the label you type above. Feel free to move this slide to any position in the deck.

Hunting for Bugs with Artemis Gabriela F. Creţu-Ciocârlie Mihai Budiu Moises Goldszmidt Microsoft Research, Silicon Valley WASL 2008 This presentation is built and should be viewed with pptPlex:

Artemis Goal One-stop shop for performance analysis of distributed systems

Principles 1) Modular: Separate generic from application specific parts 2) Extensible: add new analyses via plug-ins 3) Interactive: human expert part of the analysis loop

pptPlex Section Divider System Architecture The slides after this divider will be grouped into a section and given the label you type above. Feel free to move this slide to any position in the deck.

Logs Database View Data collection Distributed system Plug-ins GUI Distributed Local

Logs Database View Data collection Distributed system Plug-ins GUI Application- Specific Generic

pptPlex Section Divider Dryad Overview The slides after this divider will be grouped into a section and given the label you type above. Feel free to move this slide to any position in the deck.

grep sed sort awk perl grep sed sort awk Input files Vertices Output files ChannelsStage Dryad Application Structure

Dryad System Architecture data plane job schedule control plane Serv V VV Job managercluster

pptPlex Section Divider Data Collection The slides after this divider will be grouped into a section and given the label you type above. Feel free to move this slide to any position in the deck.

TextBinaryXMLPerfmon TextBinaryXMLPerfmon Data Persisted data Copy Parse Filter Aggregate DryadLINQ application 10GB-1TB 100MB-1GB TextBinaryXMLPerfmon

pptPlex Section Divider GUI The slides after this divider will be grouped into a section and given the label you type above. Feel free to move this slide to any position in the deck.

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Machine Utilization Plug-in

Complex statistics: HiLighter plug-in 22 Metrics Binary search over logistic regression with L1 regularization Key Performance Indicator Correlated metrics

Interactive Analysis Feature Computation Visualization Hilighter KPI Selection

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Raw data Summarization Feature extraction Statistical analyses Automatic diagnosis Distributed system Artemis today Goal Conclusions