Scalable Verification of GNN-based Job Schedulers
arXiv:2203.03153 · doi:10.1145/3563325
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.
Condensed version published at OOPSLA'22
References in corpus (8)
- Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning
- Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
- Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study
- Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
- Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Trees
- Towards Scalable Verification of Deep Reinforcement Learning
- Improving Neural Network Verification through Spurious Region Guided Refinement
- Online Learning Demands in Max-min Fairness