Optimizing multi-time series forecasting for enhanced cloud resource utilization based on machine learning
Mateusz Smendowski, Piotr Nawrocki
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 reduction of up to 44%. • A novel multi-time series forecasting system for cloud resource reservation planning. • A context-aware multi-time series forecasting optimization method. • A novel hybrid ensemble anomaly detection algorithm. • A multifaceted evaluation of the forecasting system using a real-life dataset. • A FinOps-driven qualitative and quantitative assessment of dynamic reservation plans.