A Smart Scheduling Optimization for Dependent Tasks with Deadlines

Crop classification, planting areas estimation and water demand/storage prediction are critical for optimizing water resource management in agriculture. In the water management based on sky-ground collaboration, heterogeneous data from various devices such as satellite, airborne remote sensing and ground observations, are closely depended. In general, tasks required by users always contain several dependent subtasks, and tasks are required to be finished before deadline. In this paper, we introduce a smart scheduling based on graph attention network and meta-learning for tasks with deadlines, which minimizes makespan by the balance between the data transmission time and computation time of subtasks. An enhanced multi-head attention mechanism in graph attention networks is designed to extract associations among heterogeneous data. Additionally, an exponentially smoothed meta-learning approach is designed to optimize parameters of strategies. Compared to existing deep learning-based scheduling algorithms, the proposed strategy improves the average proportion of tasks completed before deadlines by 7.52%.