A forecasting model for data center bandwidth utilization

Abstract

Bandwidth optimization and its efficient utilization is more challenging in operating data centers. Our model can assist for proper usage of resource utilization and accommodate large scale of bursty data. In this paper we propose forecast model for Data Center Bandwidth Utilization system; a forecast model for data centers to predict and estimate proper bandwidth utilization in real-world situations. Based on self-learning procedures, the proposed forecasting model will optimize the traffic and predict bandwidth more efficiently. Our approach is based on Time Series and Vector Autoregression (VAR-Model) models, it optimizes the bandwidth traffic detecting and diagnosing the future based on historical data.