Random graph generation for scheduling simulations

Daniel Cordeiro (1, 2) , Grégory Mounié (1, 2) , Swann Pérarnau (1, 2) , Denis Trystram (1, 2) , Jean-Marc Vincent (2, 3) , Frédéric Wagner (1, 2)

1 MOAIS - PrograMming and scheduling design fOr Applications in Interactive Simulation
2 LIG - Laboratoire d'Informatique de Grenoble
3 MESCAL - Middleware efficiently scalable

In parallel and distributed systems, validation of scheduling heuristics is usually done by simulation on randomly generated synthetic workloads, typically represented by task graphs. Since there is no single generation method that models all possible workloads for scheduling problems, researchers often re-implement the classical generation algorithms or even implement ad-hoc ones. A bad choice of generation method can mislead the validation of the algorithm due to biases it can induce. Moreover, different implementations of the same randomized generation method may produce slightly different graphs. These problems can harm the experimental comparison of scheduling algorithms. In order to provide a comparison basis we propose GGen -- a unified and standard implementation of classical task graph generation methods used in the scheduling domain. We also provide an in-depth analysis of each generation method, emphasizing important graph properties that may influence scheduling algorithms.

Mots clés: random graph generation; direct acyclic graph; scheduling; toolkit

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Daniel Cordeiro, Grégory Mounié, Swann Pérarnau, Denis Trystram, Jean-Marc Vincent, et al.. Random graph generation for scheduling simulations. 3rd International ICST Conference on Simulation Tools and Techniques (SIMUTools 2010), Mar 2010, Malaga, Spain. pp.10. ⟨hal-00471255v2⟩