Comparative Analysis of ARIMA Time Series Model and Other Techniques for Cloud Workloads Performance Prediction
摘要
Accurate performance prediction of cloud workloads is essential for optimizing resource allocation, meeting service level agreements (SLAs), and ensuring efficient cloud service delivery. In this research paper, we conduct a comparative analysis of the ARIMA (Auto Regressive and Integrated Moving Average) time series model with other popular techniques for cloud workloads performance prediction. We evaluate the performance of ARIMA in comparison with other models, including machine learning algorithms and statistical methods, using real-world cloud workload performance data.