Time Series-Oriented Load Prediction Model and Migration Policies for Distributed Simulation Systems

Abstract

HLA-based simulation systems are prone to load imbalances due to lack management of shared resources in distributed environments. Such imbalances lead these simulations to exhibit performance loss in terms of execution time. As a result, many dynamic load balancing systems have been introduced to manage distributed load. These systems use specific methods, depending on load or application characteristics, to perform the required balancing. Load prediction is a technique that has been used extensively to enhance load redistribution heuristics towards preventing load imbalances. In this paper, several efficient Time Series model variants are presented and used to enhance prediction precision for large-scale distributed simulation-based systems. These variants are proposed to extend and correct the issues originating from the implementation of Holt's model for time series in the predictive module of a dynamic load balancing system for HLA-based distributed simulations. A set of migration decision-making techniques is also proposed to enable a prediction-based load balancing system to be independent of any prediction model, promoting a more modular construction.