Scalable verification of GNN-based job schedulers

Haoze Wu, Clark Barrett, Mahmood Sharif, Nina Narodytska, Gagandeep Singh

Research output: Contribution to journalArticlepeer-review

Abstract

Recently, Graph Neural Networks (GNNs) have been applied for scheduling jobs over clusters, achieving better performance than hand-crafted heuristics. Despite their impressive performance, concerns remain over whether these GNN-based job schedulers meet users' expectations about other important properties, such as strategy-proofness, sharing incentive, and stability. In this work, we consider formal verification of GNN-based job schedulers. We address several domain-specific challenges such as networks that are deeper and specifications that are richer than those encountered when verifying image and NLP classifiers. We develop vegas, the first general framework for verifying both single-step and multi-step properties of these schedulers based on carefully designed algorithms that combine abstractions, refinements, solvers, and proof transfer. Our experimental results show that vegas achieves significant speed-up when verifying important properties of a state-of-the-art GNN-based scheduler compared to previous methods.

Original languageEnglish
Article number162
JournalProceedings of the ACM on Programming Languages
Volume6
Issue numberOOPSLA2
DOIs
StatePublished - 31 Oct 2022

Keywords

  • Cluster Scheduling
  • Formal Verification
  • Graph Neural Network

All Science Journal Classification (ASJC) codes

  • Software
  • Safety, Risk, Reliability and Quality

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