Learning Based Distributed Orchestration of Stochastic Discrete Event Simulations

Discrete event simulations (DES) are used in situations where we need to understand or describe complex phenomena. This paper describes an algorithm for dynamic orchestration of stochastic DES. To cope with long execution times in stochastic DES settings, we use MapReduce to achieve concurrent processing of the simulation on a distributed collection of machines. The proposed algorithm proactively targets imbalances between subtasks of the simulation. It achieves this by accurately predicting future execution times for map instances and apportioning processing workloads while accounting for the overheads associated with the apportioning. Our empirical benchmarks demonstrate the suitability of our scheme.