Deadline-Constrained Dynamic Workflow Scheduling Can be Cast as a Representation Learning Problem

Deadline-Constrained Cost-Aware Dynamic Workflow Scheduling (D-CADWS) aims to minimize virtual machine (VM) rental cost while maintaining high workflow deadline satisfaction in dynamic cloud environments. The problem is challenging because the deadline impact of assigning a workflow task to a VM is not directly observable at decision time. It depends on long-horizon effects, including downstream task dependencies, VM queueing, and interactions among dynamically arriving workflows. Existing methods usually fold deadline violations into a single reward or penalty term, which obscures the distinction between action safety and action cost. In this paper, we cast D-CADWS as a representation learning problem. The core idea is to learn a deadline-aware representation that can predict, before execution, whether a candidate action is risky with respect to the current task deadline, and then use it to guide cost-efficient scheduling. Based on this idea, we propose a representation-centered deep reinforcement learning (RCDRL) method. RCDRL constructs task-level deadline supervision, trains a predictive deadline model to learn deadline-aware representations, and learns a VM-cost critic on top of this representation. The final policy follows a safe-first rule that rejects risky actions before minimizing VM cost. We further propose a two-phase training strategy to keep the learned representation aligned with the evolving policy-induced data distribution. Experiments on dynamic workflow scheduling benchmarks show that RCDRL achieves substantially lower VM cost than other state-of-the-art heuristic and DRL baselines while maintaining strong workflow deadline success.