We consider non-clairvoyant scheduling with online precedence constraints, where an algorithm is oblivious to any job dependencies and learns about a job only if all of its predecessors have been completed. Given strong impossibility results in classical competitive analysis, we investigate the problem in a learning-augmented setting, where an algorithm has access to predictions without any quality guarantee. We discuss different prediction models: novel problem-specific models as well as general ones, which have been proposed in previous works. We present lower bounds and algorithmic upper bounds for different precedence topologies, and thereby give a structured overview on which and how additional (possibly erroneous) information helps for designing better algorithms. Along the way, we also improve bounds on traditional competitive ratios for existing algorithms.

翻译：我们考虑具有在线优先约束的非隐式调度问题，其中算法对作业依赖关系完全未知，仅当某个作业的所有前驱作业完成后才能获知其信息。鉴于经典竞争分析中存在强不可能性结果，我们在学习增强环境下研究该问题，即算法可获取无质量保证的预测。我们讨论了不同的预测模型：包括针对问题的新型专用模型及前人工作中提出的通用模型。针对不同优先拓扑结构，我们给出了下界与算法上界分析，从而系统性地揭示了何种额外（可能带有误差的）信息以及如何利用这些信息有助于设计更优算法。在此过程中，我们还改进了现有算法在传统竞争比上的边界值。