Data center workload dynamic scheduling dataset

This dataset provides a comprehensive resource for research in cloud computing, workload scheduling, and energy-aware resource management. It includes:\u200b\u200bWorkload traces\u200b\u200b (two scales: large and small), derived from the Alibaba Cluster Trace 2020, capturing task arrival times, resource demands (CPU, memory), and deadlines after rigorous cleaning and filtering.\u200b\u200bServer configurations\u200b\u200b (two scales: large and small), specifying hardware performance parameters (e.g., CPU cores, memory, energy profiles) to model heterogeneous infrastructures.\u200b\u200bTime-varying electricity prices\u200b\u200b at 15-minute granularity, sourced from CAISO, to enable cost and energy efficiency analysis.The dataset supports studies on dynamic workload scheduling, server provisioning, and demand-response optimization in data centers. The inclusion of multi-scale workload and server data ensures flexibility for simulating diverse scenarios, from small-scale experiments to large-scale cluster analyses.