  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.
