构建自动演进的天文大数据负载模型

The benchmark's guiding role in system selection/optimization requires its workload model has the ability to: Run on various systems of the target application scenario (be portable); Reflect the typical tasks' characteristics and data access patterns (be representative). The emerging systems and tasks in large-scale astronomical data management field have led workload models constructed by existing methods to be prone to lose portability and representativeness. An automatic evolutionary workload modeling method has been proposed: Abstract operations are used to keep the workload model’s portability; Automatic workload log analytics are used to keep the workload model’s representativeness. The feasibility of this method is verified by a cluster optimization case.