~/talk

Change Management in Physical Network Lifecycle Automation

on YouTube 21m
Talkat USENIX ATC '23 · 2023-07-10..07-12· [networking, devops-and-delivery]

speakers

name:Mohammad Al-Fares
links:none on file
name:Virginia Jean Beauregard
links:none on file
name:Kevin Grant
links:none on file
name:Angus Griffith
links:none on file
name:Jahangir Hasan
links:none on file
name:Chen Huang
links:none on file
name:Quan Leng
links:none on file
name:Jiayao Li
links:none on file
name:Alexander Lin
links:none on file
name:Zhuotao Liu
links:none on file
name:Ahmed Mansy
links:none on file
name:Bill Martinusen
links:none on file
name:Nikil Mehta
bluesky:nikil.bsky.social
name:Jeffrey C. Mogul
links:none on file
name:Andrew Narver
links:none on file
name:Anshul Nigham
links:none on file
name:Melanie Obenberger
links:none on file
name:Sean Smith
links:none on file
name:Kurt Steinkraus
links:none on file
name:Sheng Sun
links:none on file
name:Edward Thiele
links:none on file
name:Amin Vahdat
linkedin:vahdat

resources

video:https://youtu.be/dmX_UXavC5o
slides:none on file

summary

Google describes a deployed system that automates physical network lifecycle changes using the MALT language and a suite of services: a model design service, a stateful model build engine with rule-based build units and high‑90s cache hit rates, a model query API that supports profile evolution, and Topoplan branches for planning and rebasing. The system detects allocation and topology conflicts weeks or months before deployment, enables concurrent projects that used to serialize work, and cuts deployment prep by weeks to months while preserving safety and rollback.

AI-written summary · about

chapters (7)

  1. 0:00Introduction and context
  2. 1:45Key network challenges
  3. 4:18Malt network modeling
  4. 7:04Model generation systems
  5. 12:15Topo plan and workflows
  6. 14:56Case studies and results
  7. 18:48Conclusion

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