Cloud Scheduling Datasets

1. Augmented Cloud Scheduling Dataset: This dataset was generated by introducing small random perturbations (jitter) to the original base dataset of 100 rows. Each record contains four attributes: Index, input_size, output_size, and priority. Perturbations were clipped within valid bounds (input_size ∈ [1,99]; output_size ∈ [3,100]; priority ∈ [1,5]) to maintain realism. 2. Synthetic Uniform Cloud Scheduling Dataset: A fully synthetic dataset generated by sampling uniformly across valid ranges: input_size ∈ [1,99]; output_size ∈ [3,100]; priority ∈ [1,5]. The number of samples can be scaled (e.g., 500 rows). 3.Bootstrap-Expanded Cloud Scheduling Dataset: This dataset was created by resampling with replacement from the base dataset to add 500 new rows. Unique Index values were assigned, and the new samples were concatenated with the original dataset. 4.Stratified-Augmented Cloud Scheduling Dataset: A stratified augmentation technique was applied to preserve the priority distribution observed in the original dataset. Within each priority group, small controlled perturbations were added to input_size and output_size, clipped within valid ranges.1. 增强型云调度数据集（Augmented Cloud Scheduling Dataset）：该数据集通过对包含100条记录的原始基础数据集引入小型随机扰动（抖动，jitter）生成。每条记录包含索引（Index）、输入规模（input_size）、输出规模（output_size）与优先级（priority）四个属性。扰动被限制在合法范围内：input_size ∈ [1,99]、output_size ∈ [3,100]、priority ∈ [1,5]，以维持数据的现实合理性。 2. 均匀合成云调度数据集（Synthetic Uniform Cloud Scheduling Dataset）：该数据集为全合成数据集，通过在合法范围内均匀采样生成，合法范围为：input_size ∈ [1,99]、output_size ∈ [3,100]、priority ∈ [1,5]。样本数量可按需调整（例如500条记录）。 3. 自助法扩展云调度数据集（Bootstrap-Expanded Cloud Scheduling Dataset）：该数据集通过对基础数据集进行有放回重采样生成，新增500条记录。为新样本分配唯一的索引（Index）值，并将其与原始数据集拼接整合。 4. 分层增强云调度数据集（Stratified-Augmented Cloud Scheduling Dataset）：该数据集采用分层增强技术构建，以保留原始数据集中的优先级分布。在每个优先级分组内，对输入规模（input_size）与输出规模（output_size）施加小型可控扰动，并将扰动限制在合法范围内。