Workload-Driven Database Optimization for Cloud Applications

2017 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2017-07-01 DOI:10.1109/HPCS.2017.94

C. Diamantini, Alex Mircoli, D. Potena, Valentina Tempera, Matteo Moretti

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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.

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面向云应用的工作负载驱动的数据库优化

现代数据密集型应用程序的性能与数据访问的速度密切相关。然而，由于存在大型数据库和时变工作负载，通过设计进行物理数据库优化通常是不可行的。在本文中，我们介绍了一种新的物理数据库优化方法，该方法允许通过分析数据库日志来快速动态地选择索引。将该技术应用于使用按使用付费模型的云应用程序，由于弹性资源的存在，可以立即节省成本。为了证明该方法的有效性，我们给出了一个案例研究Nuvola，这是一个面向学校的SaaS多租户应用程序，其特点是工作负荷很大。实验结果表明，对于给定的工作负载，该技术可以将查询执行时间减少52.1%。通过M/M/1队列模型对优化前后的数据库性能进行了对比分析，并对结果进行了讨论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 International Conference on High Performance Computing & Simulation (HPCS)

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