A Comparison of Random Task Graph Generation Methods for Scheduling Problems

Abstract

How to generate instances with relevant properties and without bias remains an open problem of critical importance to compare heuristics fairly. When scheduling with precedence constraints, the instance is 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 such as the mass, which measures how much a task graph can be decomposed into smaller ones. This property and an in-depth analysis of existing random instance generators establish the sub-exponential generic time complexity of the studied problem.