The performance of modern data-intensive applications is closely related to the speed of data access. However, a physical database optimization by design is often infeasible, due to the presence of large databases and time-varying workloads. In this paper we introduce a novel methodology for physical database optimization which allows for a quick and dynamic selection of indexes through the analysis of database logs. The application of the technique to cloud applications, which use a pay-per-use model, results in immediate cost savings, due to the presence of elastic resources. In order to demonstrate the effectiveness of the approach, we present the case study Nuvola, a SaaS multitenant application for schools that is characterized by heavy workloads. Experimental results show that the proposed technique leads to a 52.1% reduction of query execution time for a given workload. A comparative analysis of database performance before and after the optimization is also performed through a M/M/1 queue model and the results are discussed.
Workload-Driven Database Optimization for Cloud Applications / Diamantini, Claudia; Mircoli, Alex; Moretti, Matteo; Potena, Domenico; Tempera, Valentina. - (2017), pp. 595-602. (Intervento presentato al convegno The 2017 International Conference on High Performance Computing & Simulation tenutosi a Genova, Italy nel July 17 – 21, 2017) [10.1109/HPCS.2017.94].
Workload-Driven Database Optimization for Cloud Applications
Diamantini, Claudia;Mircoli, Alex;Potena, Domenico;
2017-01-01
Abstract
The performance of modern data-intensive applications is closely related to the speed of data access. However, a physical database optimization by design is often infeasible, due to the presence of large databases and time-varying workloads. In this paper we introduce a novel methodology for physical database optimization which allows for a quick and dynamic selection of indexes through the analysis of database logs. The application of the technique to cloud applications, which use a pay-per-use model, results in immediate cost savings, due to the presence of elastic resources. In order to demonstrate the effectiveness of the approach, we present the case study Nuvola, a SaaS multitenant application for schools that is characterized by heavy workloads. Experimental results show that the proposed technique leads to a 52.1% reduction of query execution time for a given workload. A comparative analysis of database performance before and after the optimization is also performed through a M/M/1 queue model and the results are discussed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
