Title: DeepQT : Learning Sequential Context for Query Execution Time Prediction
Authors: Jingxiong Ni, Yan Zhao, Kai Zeng, Han Su and Kai Zheng
Abstract: Query Execution Time Prediction is an important and challenging problem in the database management system. It is even more critical for a distributed database system to effectively schedule the query jobs in order to maximize the resource utilization and minimize the waiting time of users based on the query execution time prediction. While a number of works have explored this problem, they mostly ignore the sequential context of query jobs, which may affect the performance of prediction significantly. In this work, we propose a novel Deep learning framework for Query execution Time prediction, called DeepQT, in which the sequential context of a query job and other features at the same time are learned to improve the performance of prediction through jointly training a recurrent neural network and a deep feed-forward network. The results of the experiments conducted on two datasets of a commercial distributed computing platform demonstrate the superiority of our proposed approach.
