~/talk

SelfTune: Tuning Cluster Managers

on YouTube 17m
Talkat NSDI 2023 · 2023-04-17..04-19· [machine-learning, devops-and-delivery]

speakers

name:Ajaykrishna Karthikeyan
links:none on file
name:Nagarajan Natarajan
links:none on file
name:Gagan Somashekar
x:gagan_yes
name:Lei Zhao
links:none on file
name:Ranjita Bhagwan
links:none on file
name:Rodrigo Fonseca
links:none on file
name:Tatiana Racheva
links:none on file
name:Yogesh Bansal
links:none on file

resources

video:https://youtu.be/DfU2yx1XW8A
slides:none on file

summary

SelfTune is a lightweight API plus learning engine that plugs into cluster managers via simple calls like predict and set_reward, uses a continuous bandits algorithm, and tunes real and discrete parameters online with low sample complexity. Deployed to tens of thousands of machines for Microsoft’s background scheduler, it continuously adjusts a concurrency knob to raise throughput up to 20%. In benchmarks it tunes ~100 parameters for DeadStar, cuts rounds-to-good-config from ~30–40 down to about 8, and lowers iteration cost.

AI-written summary · about

chapters (9)

  1. 0:00Intro
  2. 0:24Cluster management frameworks
  3. 4:30Configuring cluster managers
  4. 7:54Key Features
  5. 9:08Environments, rewards, optimal configs vary with time
  6. 9:59Evaluation: Three cluster management settings
  7. 13:13Azure Function-as-a-Service
  8. 14:25Hybrid Histogram Policy
  9. 16:14Summary

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