Enhancing Load Balancing Efficiency Based on Migration Delay for Large-Scale Distributed Simulations

Abstract

Load management is an essential and important factor for distributed simulations running on shared resources due to load imbalances that can caused considerable performance loss. This feature is essential for High Level Architecture (HLA)-based simulations since the HLA framework does not present the ability to manage resources or help detect load imbalances that could directly cause decrease of performance. A migration-aware dynamic balancing system has been designed for HLA simulations to offer an efficient load-balancing scheme that works in large-scale environments. This system presents some limitations on estimating costs and benefits, so we propose an enhancement to this existing load balancing system, which improves the accuracy of generating federate migrations. The proposed scheme aims to precisely estimate the migration delay and gain by analyzing the load on shared resources, preventing the issuing of migrations costly towards simulation execution time. Upon a performance analysis, the proposed decision-making analysis scheme has shown an improvement on decreasing the number of migrations and consequently decreasing execution time.