私有云中CPU工作负载预测的两阶段预测系统
A Two-Stage Forecasting System for CPU Workload Prediction in Private Clouds
- Blekinge Institute of Technology（布莱金厄理工学院）
- Ericsson AB（爱立信公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对私有云CPU工作负载预测问题，提出级联XGBoost两阶段集成模型，经10个应用的真实迹线评估，其预测性能优于传统直接方法，可用于云资源管理与自动扩缩容。
AI中文摘要：
准确的云资源预测对于动态云环境中的主动资源配置、维持服务质量（QoS）以及降低运营成本至关重要。现有预测方法大多直接从历史资源迹线估计未来CPU工作负载，常忽略客户服务需求与后续资源消耗之间的关系。本研究提出一种两阶段集成预测模型，通过先预测客户服务请求（以每秒事务数TPS表示），再基于TPS预测结果估算未来CPU工作负载，明确建模上述依赖关系。预测组件与资源预测组件均采用XGBoost模型，置于级联学习架构中，并辅以基于扩展窗口策略的自适应在线重训练，以应对持续演变的云工作负载中的概念漂移。该研究使用从包含10个应用的私有云环境收集的真实迹线进行评估。实验结果显示，该模型对大多数应用的对称平均绝对百分比误差（SMAPE）低于7%，表现最佳的应用达到平均绝对误差（MAE）0.7372、均方根误差（RMSE）1.1866、SMAPE 3.57%，R²为0.9185。按预测步长的漂移分析证实，在60步预测范围内，递归预测行为稳定且误差积累可控。与传统直接CPU预测方法相比，所提两阶段集成模型提升了预测鲁棒性、计算效率和可解释性，非常适合云计算环境中的主动资源管理与智能自动扩缩容。
英文摘要：
Accurate cloud resource forecasting is essential for proactive resource provisioning, maintaining Quality of Service (QoS), and reducing operational costs in dynamic cloud environments. The existing forecasting approaches predominantly estimate future CPU workload directly from historical resource traces, which often overlook the relationship between customer service demand and subsequent resource consumption. This study proposes a two-stage integrated forecasting model that explicitly models this dependency by first forecasting customer service requests, expressed as Transactions Per Second (TPS), and subsequently estimating future CPU workload from the TPS forecast. Both the forecasting component and resource prediction component employed the XGBoost model within a cascaded learning architecture, complemented by adaptive online retraining using an expanding-window strategy to address concept drift in continuously evolving cloud workloads. The proposed work was evaluated using real-world traces collected from a private cloud environment comprising ten applications. Experimental results demonstrate robust forecasting performance by achieving Symmetric Mean Absolute Percentage Error (SMAPE) below $7\%$ for most applications, with the best-performing application achieving an MAE of $0.7372$, RMSE of $1.1866$, SMAPE of $3.57\%$, and an R2 of $0.9185$. Horizon-wise drift analysis confirmed stable recursive forecasting behavior with controlled error accumulation across a 60-step prediction horizon. Compared with the conventional direct CPU forecasting method, the proposed two-stage integrated model gives improved forecasting robustness, computational efficiency, and interpretability, making it well-suited for proactive resource management and intelligent auto-scaling in cloud computing environments.
