Right-sizing Recommendations (RSR): Cloud Workload Conformal Prediction for Virtual Machines in Data Center Operations
Quick Answer
This study introduces a data-driven approach using bootstrapping conformal prediction for Right-sizing Recommendations (RSR) in cloud environments, enhancing VM provisioning efficiency.
Quick Take
By leveraging AI/ML techniques, the framework predicts medium- to long-term resource utilization trends, achieving promising forecasting results and supporting cost-effective resource allocation for diverse workloads in hyperscaler data centers.
Key Points
- Conformal prediction enhances prediction intervals for cloud resource demand.
- AI-driven models show promising results in forecasting VM utilization.
- The framework supports cost-effective resource allocation in dynamic environments.
- Bootstrapping techniques identify correlations across multiple time series.
- Top-performing models for long-life VM candidates are ranked for efficiency.
DeepSignal Analysis
What happened
The study presents a framework utilizing bootstrapping conformal prediction to enhance Right-sizing Recommendations (RSR) for virtual machines in cloud environments. This approach aims to improve resource allocation efficiency by predicting medium- to long-term utilization trends based on workload patterns.
Key evidence
- The proposed framework employs conformal prediction for constructing prediction intervals, which helps capture uncertainty in cloud resource demand.
- AI-driven models using machine learning regression techniques were evaluated through backtesting, yielding promising results for forecasting cloud resource utilization.
- The research identifies top-performing models for long-life VM candidates, enhancing the efficiency of resource allocation in dynamic cloud environments.
Why it matters
Efficient management of cloud infrastructure is critical for minimizing costs and maximizing performance, especially for large cloud providers. By improving the accuracy of resource utilization predictions, this framework could lead to significant cost savings and operational efficiencies in hyperscaler data centers, which often struggle with resource over- and under-provisioning.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Managing cloud infrastructure efficiently, especially in environments of large cloud providers or hyperscalers, requires optimizing the use of physical resources to minimize costs and maximize performance. Selecting the right virtual machine (VM) sizes is crucial to achieving cost efficiency in these dynamic environments. However, traditional VM allocation and scheduling approaches often fail to account for the fluctuating and unpredictable nature of VM utilization, leading to inefficiencies such as over- or under-provisioning of resources. High-quality interval prediction helps accurately capture uncertainty in cloud resource demand and supports cloud operators in efficient instance provisioning.
As an effective and reliable framework for constructing prediction intervals (PIs), conformal prediction (CP) is used for mid- and long-term forecasting tasks in cloud computing environments. This study proposes a new data-driven PI construction approach using bootstrapping conformal prediction for modern, dynamic, data-driven Right-sizing Recommendations (RSR) to enhance provisioning for diverse application workloads on hyperscalers. By learning workload utilization patterns, identifying correlations across multiple time series, and predicting medium- to long-term utilization trends, this research seeks to improve the efficiency of cloud and data center operations through an AI/ML-based provisioning pipeline.
Our study demonstrates that AI-driven models, powered by machine learning regression techniques and evaluated using backtesting, achieve promising forecasting results for cloud resource utilization. Additionally, we rank the selected models to identify top-performing approaches for long-life VM candidates. The proposed framework enhances right-sizing recommendations and supports more cost-effective resource allocation in dynamic cloud environments.
| Comments: | 10 pages, 10 figures. Accepted for publication in the Proceedings of the 2025 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2025). Author Accepted Manuscript |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.24773 [cs.AI] |
| (or arXiv:2607.24773v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.24773 arXiv-issued DOI via DataCite |
Submission history
From: Mehryar Majd [view email]
[v1]
Fri, 12 Jun 2026 10:04:54 UTC (1,058 KB)
— Originally published at arxiv.org
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