A Genetic Algorithm Approach for Adjusting Time Series Based Load Prediction

Distributed virtual simulation are prone to load oscillations, as well as load imbalances during run-time. Detecting such imbalances and responding accordingly using load redistribution can be of great utility in keeping execution performance close to the aimed optimal. A dynamic balancing scheme can introduce a reactive approach, but a predictive scheme can prevent imbalances before they occur. Several models can be employed for predicting load, but due to the characteristics in which the load is collected and presented, time series offer reasonable load forecasting in a short time. However, the Holt's model, well known model for time series representation, shows limitations on the forecasting of load. In order to correct this issue, a genetic algorithm approach is introduced to dynamically adjust the model based on the recent modifications on the load behaviour. The convergence of the algorithm can substantially influence the response time of the predictive balancing system, so an analysis is conducted to identify the minimum number of iterations for generating a reasonable adjustment.