Challenges and approaches to time-series forecasting in data center telemetry: A Survey
Challenges and approaches to time-series forecasting in data center telemetry: A Survey
复制标题
数据中心遥测中时间序列预测的挑战和方法：调查
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Ajit Patnakar
中科院分区：
文献类型：
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作者：
Shruti Jadon;Jan Kanty Milczek;Ajit Patnakar
多年来，时间序列预测一直是一个重要的研究领域。它的应用包括心电图预测、销售预测、天气状况，甚至是COVID-19传播预测。这些应用促使许多研究者寻找最优的预测方法，但建模方法也随着应用领域的变化而变化。这项工作的重点是审查在数据中心收集的遥测数据预测的不同预测方法。遥测数据预测是网络和数据中心管理产品的一个重要特征。然而，有多种预测方法可供选择，从简单的线性统计模型到高容量深度学习架构。在本文中，我们试图总结和评估众所周知的时间序列预测技术的性能。我们希望这一评价为遥测数据预测方法的创新提供一个全面的总结。
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.