Challenges and Approaches to Time-Series Forecasting for Traffic Prediction at Data Centers

Abstract

Time-series forecasting has been an important research domain with significant applications, such as ECG predictions, sales forecasting, weather conditions, and recently COVID-19 spread predictions. Many researchers have investigated a multitude of modeling approaches to meet the requirements of these wide ranges of applications. In this context, our work focuses on reviewing different forecasting approaches for telemetry data collected in networks and data centers. Forecasting of telemetry data is a critical feature of network and data center management products. However, there are multiple options of forecasting approaches that range from a simple linear statistical model to high-capacity deep learning architectures. In this paper, we summarize and evaluate the performance of many well-known time series forecasting techniques. This research evaluation aims to provide a comprehensive summary for further innovation in forecasting approaches for telemetry data.