Efficient task scheduling is crucial in edge computing to improve computational efficiency. However, due to dynamic workload variations and exclusive resource allocation, traditional scheduling mechanisms often fail to effectively reuse scheduling optimization results, generating wasteful scheduling overhead. To reduce scheduling overhead while improving system performance, we propose an Ahead-of-Time (AoT) scheduling mechanism for workflow applications. Through decoupling requests and isolating resources, our mechanism enables sustainable reuse of the pre-cached scheduling plan, thereby improving computational efficiency in edge computing environments. Specifically, our mechanism decouples incoming requests into two categories: those that match the pre-cached scheduling plan and those that do not. The matching requests are scheduled to plan-managed resources, while the rest requests are scheduled to the remaining resources using conventional online scheduling algorithms. To achieve sustainable reuse of the scheduling plan, we isolate the plan-managed resources and remaining resources to prevent interference, and the AoT scheduling algorithm dynamically adjusts the plan’s execution progress to accommodate workload variations. Experimental results in an intelligent transportation scenario demonstrate that, compared to heuristic-based and reinforcement learning-based baselines, the proposed AoT scheduling reduces scheduling overhead by 63.57%–85.65%, increases throughput by 25.35%–682.02% (thanks to pre-planned resource usage, while baselines suffer from chaotic or lock-based resource usage), and decreases latency by 16.16%–71.61%.