Predicting query response time is an essential task for managing database systems, especially in modern large distributed data centers that execute heterogeneous query workloads concurrently. The core of such a model is to quantify query interaction, which is neglected by the state of the art models. This paper proposes a novel model that estimates query response time based on the similarity of query mixes. We introduce a notion called query rating for constructing the feature vector of a query and developed a measure of the similarity between two query mixes. We propose a static similarity model to estimate the response time of a query by using that of the most similar query mixes containing the query. We also build a dynamic model based on the static model to predict the remaining execution time of a query on-the-fly whenever a new query mix forms. A scheduling method is proposed with the similarity models as the key enablement, which schedules a workload with minimum execution time. The experimental evaluation shows that our models perform approximately 12% and 35% of the actual response time on average for static and dynamic respectively, and the scheduler with our model outperforms, up to 2.9x, that with conventional models consistently.

预测查询响应时间是管理数据库系统的一项重要任务，尤其是在同时执行异构查询工作负载的现代大型分布式数据中心中。这种模型的核心是量化查询交互，而现有技术模型却忽略了这种交互。本文提出了一种基于查询混合相似度来估计查询响应时间的新颖模型。我们引入了一种称为查询评级的概念，以构造查询的特征向量，并开发了两种查询组合之间相似度的度量。我们提出一个静态相似性模型，通过使用包含该查询的最相似查询组合的响应时间来估计查询的响应时间。我们还基于静态模型构建了一个动态模型，以在新查询混合形成时即时预测查询的剩余执行时间。提出了一种以相似度模型为关键实现的调度方法，该调度方法以最小的执行时间调度工作负载。实验评估表明，我们的模型分别在静态和动态上平均分别执行实际响应时间的12％和35％，并且与我们的模型相比，与我们的模型相比，调度程序的性能优于传统模型的2.9倍。