Data Workload

Data workload optimization focuses on efficiently managing and executing diverse computational tasks, aiming to minimize resource consumption (e.g., time, energy, cost) while maximizing performance. Current research emphasizes techniques like deep reinforcement learning (e.g., using TD3-TD-SWAR models) for intelligent index selection and query optimization, as well as leveraging execution traces and hybrid power models for improved resource allocation and energy efficiency in cloud environments. These advancements are crucial for handling the increasing complexity and scale of modern data processing, particularly in the context of large language model integration and the growing adoption of lakehouse architectures.