Cloud-Fog-Edge Collaborative Computing for Sequential MIoT Workflow: A Two-Tier DDPG-Based Scheduling Framework
This addresses latency-critical scheduling for MIoT workflows over cloud-fog-edge infrastructures, representing an incremental improvement with a novel hierarchical method.
The paper tackles the NP-hard problem of scheduling sequential healthcare workflows in Medical Internet of Things (MIoT) to minimize makespan, proposing a two-tier DDPG-based framework that shows increasingly superior performance over baselines as workflow complexity rises.
The Medical Internet of Things (MIoT) demands stringent end-to-end latency guarantees for sequential healthcare workflows deployed over heterogeneous cloud-fog-edge infrastructures. Scheduling these sequential workflows to minimize makespan is an NP-hard problem. To tackle this challenge, we propose a Two-tier DDPG-based scheduling framework that decomposes the scheduling decision into a hierarchical process: a global controller performs layer selection (edge, fog, or cloud), while specialized local controllers handle node assignment within the chosen layer. The primary optimization objective is the minimization of the workflow makespan. Experiments results validate our approach, demonstrating increasingly superior performance over baselines as workflow complexity rises. This trend highlights the frameworks ability to learn effective long-term strategies, which is critical for complex, large-scale MIoT scheduling scenarios.