A Modular Approach to Random Task Graph Generation

Validation of the robustness, efficiency of allocation and scheduling heuristics in large scale parallel and distributed systems is usually done using synthetic randomly generated workloads, represented by task graphs. We provide a modular approach to the problem of generating random Directed Acyclic Graphs(DAGs), called Modular Random Task Graph generator (MRTG), making it very flexible for the researchers to use. Modular open source approach provides a great advantage for future development as more modules can be added without disturbing the existing stable software. The task nodes are placement randomly using a layer-by-layer approach and then connected randomly. Paramount importance has been given to user-controlled randomness in developing this algorithm. The MRTG can generate task sets containing several different types of task graphs like ischolar_mained trees, isomorphic graphs and similar graphs with same node placement but different connections, with the flexibility to dictate the type of graph generated. In this paper, we also present a comparison of MRTG with existing solutions to the random task graph generation problem.