Cloud Computing Performance Metrics

Context:

This dataset comprises performance metrics in a cloud computing environment. It includes features such as CPU usage, memory usage, network traffic, power consumption, number of executed instructions, execution time, energy efficiency, task type, task priority, and task status. The dataset is intended to be used for exploring the impact of machine learning optimization techniques on energy efficiency and execution time in cloud environments.

Sources:

The data in this dataset was collected from a simulated cloud computing environment. The values represent a wide range of possible states and conditions in a cloud computing system.

Inspiration:

The dataset was created in response to the growing importance of energy efficiency in cloud computing. As the demand for cloud services increases, so does the energy consumption of data centers, leading to higher operational costs and CO2 emissions. Machine learning algorithms have been used to enhance the efficiency of cloud computing, but there is still room for improvement. This dataset provides a basis for exploring how machine learning optimization techniques can further increase energy efficiency and reduce execution time in cloud computing environments.

本数据集汇集了云计算环境中的性能指标，涵盖诸如CPU使用率、内存使用率、网络流量、功耗、执行指令数量、执行时间、能效、任务类型、任务优先级以及任务状态等特征。该数据集旨在用于研究机器学习优化技术在提升云计算环境中的能效和缩短执行时间方面的影响。数据来源方面，本数据集的数据采集自模拟的云计算环境，所记录的数值反映了云计算系统中可能的广泛状态和条件。其灵感源于云计算中能效日益凸显的重要性。随着对云计算服务的需求增长，数据中心能耗也随之上升，导致运营成本和二氧化碳排放量增加。尽管机器学习算法已被应用于提升云计算效率，但仍有优化空间。本数据集为探讨机器学习优化技术如何进一步增进云计算环境中的能效及缩短执行时间提供了研究基础。