Challenges and approaches to time-series forecasting in data center telemetry: A Survey

TL;DR

This survey reviews various time-series forecasting methods for data center telemetry data, highlighting their performance and suitability, to guide future innovations in network and data center management.

Contribution

It provides a comprehensive evaluation of existing forecasting techniques specifically applied to data center telemetry data, aiding in selecting appropriate models for this domain.

Findings

Deep learning models outperform traditional methods in accuracy

Linear models are faster but less accurate

Evaluation highlights the trade-offs between complexity and performance

Abstract

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.