Abstract
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.
Bhupesh Kumar Dewangan and Tanupriya Choudhury both are mentioned as corresponding author(s).
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Mishra, V.K., Mishra, M., Dewangan, B.K., Amulya, P., Choudhury, T., Kotecha, K. (2024). Comparative Analysis of ARIMA Time Series Model and Other Techniques for Cloud Workloads Performance Prediction. In: Devi, B.R., Kumar, K., Raju, M., Raju, K.S., Sellathurai, M. (eds) Proceedings of Fifth International Conference on Computer and Communication Technologies. IC3T 2023. Lecture Notes in Networks and Systems, vol 898. Springer, Singapore. https://doi.org/10.1007/978-981-99-9707-7_32
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DOI: https://doi.org/10.1007/978-981-99-9707-7_32
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