Challenges and approaches to time-series forecasting in data center telemetry: A Survey
It addresses the problem of selecting forecasting methods for data center telemetry, which is critical for network and data center management, but is incremental as it synthesizes existing approaches.
This survey paper reviews and evaluates various time-series forecasting techniques specifically for telemetry data in data centers, aiming to provide a comprehensive summary to guide innovation in this domain.
Time-series forecasting has been an important research domain for so many years. Its applications include ECG predictions, sales forecasting, weather conditions, even COVID-19 spread predictions. These applications have motivated many researchers to figure out an optimal forecasting approach, but the modeling approach also changes as the application domain changes. This work has focused on reviewing different forecasting approaches for telemetry data predictions collected at 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 attempted to summarize and evaluate the performance of well known time series forecasting techniques. We hope that this evaluation provides a comprehensive summary to innovate in forecasting approaches for telemetry data.