Effective Cloud–Edge Workflow Scheduling via Decoupled Offline Learning and Unified Sequence Modeling

Efficient workflow scheduling in cloud–edge environments is severely bottlenecked by long-horizon dependencies and myopic resource fragmentation. This paper proposes the Decoupled Offline Sequence-based (DOS) scheduling framework to address these challenges. By decoupling expert policy learning from runtime deployment, DOS utilizes a multi-dimensional priority-aware linearization strategy to deterministically transform DAG-structured workflows into dependency-consistent sequences. Leveraging offline expert trajectories, we train UDC, a Gated CNN achieving unified sequence modeling via innovative triplet-to-unary encoding, equipped with explicit action masking to distill long-horizon spatio-temporal packing patterns. This mechanism enables rapid feed-forward inference without costly online environment interactions or policy updates. Extensive evaluations on real-world Alibaba cluster workloads demonstrate that DOS not only consistently minimizes average makespan compared to classical heuristics, but also drastically reduces resource-blocked steps under extreme concurrency versus online Actor–Critic experts. Crucially, compared to the Decision Transformer (DT) baseline, the UDC model achieves strictly scale-invariant and significantly lower inference latency, highlighting its robust scalability and practicality for large-scale continuum systems.