CherryPick: Adaptively Unearthing the Best Cloud Configurations for - - PowerPoint PPT Presentation

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CherryPick: Adaptively Unearthing the Best Cloud Configurations for - - PowerPoint PPT Presentation

Mar 10, 2024 9 likes •280 views

CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics O. Alipourfard et al. Presented by Dmitry Kazhdan Overview Background Prior work CherryPick Evaluation Criticism Recent work

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  • O. Alipourfard et al.

CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics

Presented by Dmitry Kazhdan

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Overview

  • Background
  • Prior work
  • CherryPick
  • Evaluation
  • Criticism
  • Recent work
  • Conclusions
  • Questions
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Background

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Background

Opportunities:

  • Cloud computing
  • Big data analytics
  • Cost savings
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Background

Challenges:

  • Complex performance model
  • Cost model tradeoffs
  • Heterogeneous applications
  • Limited number of samples (from a large configuration space)
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Prior Work

  • Ernest
  • Coordinate descent
  • Exhaustive search
  • Random search
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CherryPick

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CherryPick

  • Uses Bayesian Optimisation to build performance models
  • Finds optimal/near-optimal configurations in only a few test runs
  • Uses the acquisition function to draw samples
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CherryPick

Initial: Modified:

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CherryPick Workflow

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CherryPick Implementation

  • Search Controller
  • Cloud Monitor
  • Bayesian Optimisation Engine
  • Cloud Controller
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Evaluation

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Evaluation

  • Applications: TPC-DS, TPC-H, TeraSort, SparkReg, SparkKm
  • 66 cloud configurations
  • Objective: reduce cost of execution under runtime constraint
  • Compared with:
  • Exhaustive search
  • Coordinate Descent
  • Random Search (with a budget)
  • Ernest
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Evaluation

  • Metric 1: the expense to run a job with the selected configuration
  • Metric 2: the expense to run all sampled configurations
  • 20 independent runs
  • 10th, 50th and 90th percentiles computed
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Evaluation

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Evaluation

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Evaluation

  • Investigated parameter tuning
  • Investigated performance behaviour
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Evaluation

  • Handling workload variation
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Criticism

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Criticism/Discussion

“With 4x cost, random search can find similar configurations to CherryPick on the median”

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Criticism/Discussion

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Criticism/Discussion

  • 3/4 comparison tasks are easy to beat (nothing to compare with)
  • Not using available information efficiently
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Recent Work

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Recent Work

  • PARIS
  • Scout
  • Arrow
  • Micky
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Conclusions

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Conclusions

  • Introduced CherryPick
  • Compared to existing systems
  • Presented evaluation results
  • Criticism
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Questions?