A New Workload Prediction Model Using Extreme Learning Machine and Enhanced Tug of War optimization

Time series data is widely accessible in many life areas like economy, weather, stock price, retail sales, distributed system workloads. While many studies have focused on improving existing prediction techniques on accuracy aspect, less efforts is paid towards simple but efficient forecasting models in order to keep the balance between computation cost and prediction accuracy. In this work, we propose a novel time series prediction model, which aims to both model simplicity and accuracy. The core of the model is built based on extreme learning machine. Due to the random determination process for input weights and hidden biases, extreme learning machine requires a large number of hidden neurons to achieve good results and this increases the model complexity. To overcome this drawback, we propose a new opposition-based tug of war optimization to select optimally input weights, and hidden biases then apply to extreme learning machine. Two real public traffic monitoring datasets from Internet service providers were employed to evaluate our design. The achieved outcomes demonstrate that our proposed solution works effectively with satisfied performance in comparison with existing models for distributed system workload prediction.