Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

Mateusz Smendowski

doi:10.1016/j.knosys.2024.112489

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Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning

2024-11-01

Mateusz Smendowski

Piotr Nawrocki *

DOI：10.1016/j.knosys.2024.112489

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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 reduction of up to 44%.

Keywords:

Cloud computing

Cloud resource usage optimization

Machine learning

Time series forecasting optimization

Cloud finOps

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