SWAGES: System-Wide Attention and Guided Evolution for Dynamic Workflow Scheduling

Cost-Aware Dynamic Workflow Scheduling (CADWS) repeatedly selects task-VM assignments for workflow tasks and heterogeneous virtual machines (VMs), while minimizing VM rental cost and SLA penalties. Existing learning-based schedulers face two key limitations. First, their policies often focus on individual tasks or workflows without explicitly considering other active workflows or alternative task-VM assignments, limiting system-wide task prioritization and resource allocation. Second, capturing these system-wide interactions requires larger policy models that are more difficult to train efficiently. Gradient-based deep reinforcement learning (DRL) can be unstable, whereas evolution strategies (ES) are more robust but become inefficient in high-dimensional policy spaces because of costly population rollouts and underused transition data. We propose **SWAGES** to address both limitations. Its **System-Wide Graph Attention Network (SWAN)** jointly models active workflows, ready tasks, VMs, and task-VM compatibility, enabling all possible task-VM assignments to be scored under a shared global context. Its **Gradient-Guided Evolution Strategies (GUIDES)** reuse rollout transitions to train twin critics and derive surrogate policy-gradient directions that span a low-dimensional subspace for fitness-based ES search. Extensive experiments show that SWAGES consistently outperforms representative heuristics and recent learning-based CADWS methods, including gradient-based DRL and ES-trained schedulers. Ablation studies further validate the effectiveness of both SWAN and GUIDES.