Scalable Verification of GNN-based Job Schedulers

TL;DR

This paper introduces vegas, a novel framework for formally verifying GNN-based job schedulers, ensuring they meet properties like strategy-proofness and stability, with significant efficiency improvements.

Contribution

We present vegas, the first general verification framework for GNN-based schedulers, addressing domain-specific challenges and enabling multi-step property verification.

Findings

Vegas achieves significant speed-up over previous methods.

Successfully verifies properties of a state-of-the-art GNN scheduler.

Addresses complex, domain-specific verification challenges.

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.