2019/02/15 by Canon, Louis-Claude, Sayah, Mohamad El, Héam, Pierre-Cyrille
#Distributed #FOS: Computer and information sciences #Parallel #Performance (cs.PF) #and Cluster Computing (cs.DC)
paper · doi:10.48550/arxiv.1902.05808
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.