Hybrid approach of extreme learning machine with differential evolution for concurrent query performance prediction

To address the challenges of continuous growth of data volume, the diversification and complexity of query workloads, a hybrid approach of Extreme Learning Machine(ELM)with Differential Evolution(DE)named DE-ELM was presented for concurrent query performance prediction, in which ELM was used to predict concurrent query performance and DE was adapted to search for optimal network structure and feature subset synchronously. DE-ELM only used information available at compile time, and did not need to specify the number of features in advance, nor need to make prior assumptions on the nature of query interactions or the internal mechanism of database system. Experimental evaluations were executed on top of real-life and TPC-DS benchmark with dynamic concurrent workloads to investigate the effect of simultaneous feature selection and network structure optimization. The results showed that the average accuracy of DE-ELM was over 80%, which verified the feasibility and effectiveness of the proposed method to some degree. © 2019, Editorial Department of CIMS. All right reserved.