The Simulation Model Partitioning Problem: an Adaptive Solution Based on Self-Clustering (Extended Version)

TL;DR

This paper proposes an adaptive self-clustering method for partitioning simulation models in parallel and distributed systems, effectively reducing execution time by balancing communication and computational load.

Contribution

It introduces a novel self-clustering approach for simulation model partitioning that considers both communication reduction and load balancing, tested on complex dynamic models.

Findings

Reduces simulation execution time in parallel and distributed architectures

Effective in handling complex and dynamic simulation models

Performance validated through comprehensive experiments

Abstract

This paper is about partitioning in parallel and distributed simulation. That means decomposing the simulation model into a numberof components and to properly allocate them on the execution units. An adaptive solution based on self-clustering, that considers both communication reduction and computational load-balancing, is proposed. The implementation of the proposed mechanism is tested using a simulation model that is challenging both in terms of structure and dynamicity. Various configurations of the simulation model and the execution environment have been considered. The obtained performance results are analyzed using a reference cost model. The results demonstrate that the proposed approach is promising and that it can reduce the simulation execution time in both parallel and distributed architectures.