调度问题中随机任务图生成方法的比较
A Comparison of Random Task Graph Generation Methods for Scheduling Problems
摘要：
如何生成具有相关属性且无偏见的实例仍然是一个开放的问题，对于公平比较算法至关重要。在具有优先约束的调度上下文中，实例由确定任务执行的部分顺序的任务图组成。为了避免在主要由平凡实例填充的集合中选择实例，我们依赖于量化特定于困难实例的特性的属性。在众多已识别的此类属性中，质量度量了任务图可以分解为更小的任务图的程度。这个属性，再加上深入分析现有的随机任务图生成方法，建立了次指数一般时间复杂度的研究问题。经验观察现有的生成方法对调度算法的影响，总结我们的研究。
英文摘要：
How to generate instances with relevant properties and without bias remains an open problem of critical importance for a fair comparison of heuristics. In the context of scheduling with precedence constraints, the instance consists of a task graph that determines a partial order on task executions. To avoid selecting instances among a set populated mainly with trivial ones, we rely on properties that quantify the characteristics specific to difficult instances. Among numerous identified such properties, the mass measures how much a task graph can be decomposed into smaller ones. This property, together with an in-depth analysis of existing random task graph generation methods, establishes the sub-exponential generic time complexity of the studied problem. Empirical observations on the impact of existing generation methods on scheduling heuristics concludes our study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
