Online Performance Modeling for NoSQL Databases using Extreme Learning Machines - Farias, Victor A. E.; Pinheiro, Pedro R. A.; Sousa, Flávio R. C.; Gomes, João P. P.; Machado, Javam C. (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2017: Anais do XXXII Simpósio Brasileiro de Bancos de Dados, 2017)
NoSQL databases rise as a solution to manage large amounts of data in the cloud. Mechanisms to guarantee Quality of Service in can significantly benefit from performance predictability. Building an accurate predictive model to estimate a DBMS performance in a cloud environment is challenging since i) workload and resources allocation change dynamically; ii) concurrency and distribution introduce nonlinearity on performance metrics and iii) predictive models should be trained and updated online to capture unseen workloads. This paper presents an online performance modeling approach for NoSQL databases using extreme learning machines. Experimental results confirm that our performance modeling can accurately predict throughput under several scenarios.