AI-Driven Cloud Resource and Energy Consumption Dataset

This dataset was developed as part of a capstone project titled "AI-Driven Optimization in Hybrid Cloud Environments". It contains time-series logs of resource consumption and energy usage from simulated and real-world hybrid cloud clusters.

The dataset is designed to support the development and evaluation of machine learning models for:

Resource allocation

Energy efficiency optimization

Workload forecasting

Sustainable cloud computing

Data is organized into training, validation, and testing sets, covering CPU usage, memory consumption, network traffic, request rates, latency, energy draw, and financial costs. The dataset reflects typical behavior across test, staging, and production environments and aims to enable energy savings of up to 30% and cost reductions of 25%.

This resource supports academic and industrial research in AI, cloud sustainability, FinOps, and dynamic infrastructure scaling.

本数据集系为题为《混合云环境下的AI驱动优化》的顶点项目所开发，收录了来自模拟与真实混合云集群的资源消耗与能耗使用的时序日志。

本数据集旨在支撑机器学习模型的开发与评估，可服务于以下场景：
- 资源分配
- 能效优化
- 工作负载预测
- 可持续云计算

数据按训练集、验证集与测试集进行组织，涵盖CPU使用率、内存占用、网络流量、请求速率、延迟、能耗值与财务成本。该数据集覆盖测试、预发布与生产环境中的典型运行行为，旨在助力实现最高30%的节能效果与25%的成本缩减。

本数据集可支撑AI、云可持续性、云财务运营（FinOps）与动态基础设施扩缩容领域的学术与工业研究。