Version 1
: Received: 8 September 2024 / Approved: 8 September 2024 / Online: 9 September 2024 (08:43:26 CEST)
How to cite:
Smendowski, M.; Nawrocki, P. Optimizing Multi-time Series Forecasting for Enhanced Cloud Resource Utilization Based on Machine Learning. Preprints2024, 2024090616. https://doi.org/10.20944/preprints202409.0616.v1
Smendowski, M.; Nawrocki, P. Optimizing Multi-time Series Forecasting for Enhanced Cloud Resource Utilization Based on Machine Learning. Preprints 2024, 2024090616. https://doi.org/10.20944/preprints202409.0616.v1
Smendowski, M.; Nawrocki, P. Optimizing Multi-time Series Forecasting for Enhanced Cloud Resource Utilization Based on Machine Learning. Preprints2024, 2024090616. https://doi.org/10.20944/preprints202409.0616.v1
APA Style
Smendowski, M., & Nawrocki, P. (2024). Optimizing Multi-time Series Forecasting for Enhanced Cloud Resource Utilization Based on Machine Learning. Preprints. https://doi.org/10.20944/preprints202409.0616.v1
Chicago/Turabian Style
Smendowski, M. and Piotr Nawrocki. 2024 "Optimizing Multi-time Series Forecasting for Enhanced Cloud Resource Utilization Based on Machine Learning" Preprints. https://doi.org/10.20944/preprints202409.0616.v1
Abstract
Due to its flexibility, cloud computing has become essential in modern operational schemes. However, the effective management of cloud resources to ensure cost-effectiveness and maintain high performance presents significant challenges. The pay-as-you-go pricing model, while convenient, can lead to escalated expenses and hinder long-term planning. Consequently, FinOps advocates proactive management strategies, with resource usage prediction emerging as a crucial optimization category. In this research, we introduce the multi-time series forecasting system (MSFS), a novel approach for data-driven resource optimization alongside the hybrid ensemble anomaly detection algorithm (HEADA). Our method prioritizes the concept-centric approach, focusing on factors such as prediction uncertainty, interpretability and domain-specific measures. Furthermore, we introduce the similarity-based time-series grouping (STG) method as a core component of MSFS for optimizing multi-time series forecasting, ensuring its scalability with the rapid growth of the cloud environment. The experiments performed demonstrate that our group-specific forecasting model (GSFM) approach enabled MSFS to achieve a significant cost
Keywords
Cloud computing; Cloud resource usage optimization; Machine learning; Time series forecasting optimization; Cloud FinOps
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
