合适规模推荐（RSR）：数据中心运营中虚拟机的云工作负载共形预测
Right-sizing Recommendations (RSR): Cloud Workload Conformal Prediction for Virtual Machines in Data Center Operations
- Hasso Plattner Institute (HPI), University of Potsdam（哈索·普拉特纳研究所（HPI），波茨坦大学）
- SAP SE（思爱普公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对云环境中虚拟机资源分配问题，提出用自举共形预测构建预测区间的方法，通过学习工作负载模式等提高运营效率，经机器学习回归技术和回测评估模型，排名选出最佳方法，增强合适规模推荐及资源分配的成本效益。
AI中文摘要：
高效管理云基础设施，尤其是在大型云提供商或超大规模环境中，需要优化物理资源使用以降低成本并提高性能。选择合适的虚拟机（VM）大小对实现成本效率至关重要。传统VM分配和调度方法常无法应对VM利用率的波动和不可预测性。高质量区间预测有助于准确捕捉云资源需求的不确定性。本研究提出一种新的数据驱动的预测区间（PI）构建方法，使用自举共形预测进行现代、动态、数据驱动的合适规模推荐（RSR），以增强对超大规模环境中不同应用工作负载的供应。通过学习工作负载利用模式、识别多个时间序列的相关性以及预测中长期利用趋势，旨在通过基于AI/ML的供应管道提高云及数据中心运营效率。研究表明，由机器学习回归技术驱动并通过回测评估的AI驱动模型在云资源利用预测方面取得了有前景的结果。此外，对所选模型进行排名以识别长期VM候选的最佳方法。所提出的框架增强了合适规模推荐，并支持在动态云环境中进行更具成本效益的资源分配。
英文摘要：
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.
