Parallel Discrete-Event Simulation on Data Processing Engines
Kazuyuki Shudo, Yuya Kato, Sugino Takahiro, Masatoshi Hanai · 2016
Development of a decent parallel simulator is challenging work. It should achieve enough performance, scalability and fault tolerance. Our proposal is utilizing general-purpose data processing engines such as MapReduce implementations for parallel simulation. Widely used and mature engines take away a large part of the development effort and support scalability and fault tolerance. We demonstrate that a parallel discrete-event simulator can be implemented on such engines, Apache Hadoop and Apache Spark, by modeling message passing of distributed systems on MapReduce key-value processing model. Implemented simulators could handle 108 nodes with 10 computers. Preliminary evaluation showed that our Spark-based simulator is about 20 times as fast as an existing simulator thanks to Time Warp.