Dynamic Software Product Line for Improving Energy Efficiency of Cloud Applications

The dataset is structured around two feature model types under study: SPLOT Repository and TeaStore. For each feature model, the data is organized into three folders:- FeatureModels/ contains the XML (for SPLOT) and JSON files that define the configuration space explored during the experiments.- ExecutionMetrics/ contains one subfolder per experimental benchmark and algorithm (named <timestamp>), each holding a summary.csv with run-level resource metrics (CPU time, memory, exit codes) alongside individual run_<N>/ directories storing raw execution artifacts such as job scripts, standard error logs, and timing files.- EnergyConsumptionData/ is organized hierarchically by feature model, algorithm, and run index. For each combination, there's one energy_<N>.json file per run, raw watt-metre measurements retrieved from the Grid5000 infrastructure API; alongside a summary_<N>.csv capturing the derived energy metrics for that run. A global_summary.csv at the root of this folder aggregates all runs across feature models and algorithms.本数据集围绕本次研究中的两类特征模型（Feature Model）搭建，分别为SPLOT存储库与TeaStore。 针对每一类特征模型，数据被划分为三个文件夹： - FeatureModels/ 文件夹：内含用于定义实验期间探索的配置空间的XML（适配SPLOT）与JSON格式文件。 - ExecutionMetrics/ 文件夹：每个实验基准测试与算法对应一个命名为<timestamp>的子文件夹。每个子文件夹内均包含一份summary.csv，用于存储运行级资源指标（包括CPU时长、内存占用、退出码）；同时附带run_<N>/ 子目录，用以存储作业脚本、标准错误日志、计时文件等原始执行产物。 - EnergyConsumptionData/ 文件夹：按特征模型、算法与运行索引进行层级化组织。针对每一组组合，每个运行对应一个energy_<N>.json文件，该文件存储从Grid5000基础设施API获取的原始功率计测量数据；同时附带summary_<N>.csv，用于记录该次运行导出的能量指标。该文件夹根目录下的global_summary.csv 汇总了所有特征模型与算法对应的全部运行数据。