Scalable Verification of GNN-based Job Schedulers
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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 their trustworthiness when deployed in a real-world environment due to their black-box nature. To address these limitations, we consider formal verification of their expected properties such as strategy proofness and locality in this work. We address several domain-specific challenges such as deeper networks and richer specifications not encountered by existing verifiers for image and NLP classifiers. We develop GNN-Verify, 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 on challenging benchmarks show that our approach can provide precise and scalable formal guarantees on the trustworthiness of state-of-the-art GNN-based scheduler.
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