Towards constrained optimization of cloud applications: A hybrid approach

Abstract

A database system is one of the most common cloud applications where elasticity allows to dynamically allocate resources in response to changing workload demands. In such systems, users usually configure resources based on empirical decisions, for example by boosting computational resources with the expectation of improving database throughput. The latter also suggests that users manually scale resources and tune the database configurations offline to meet the application demands. However, this approach could rapidly increase infrastructure expenditures while this is also time consuming. In this paper, we propose SONA, a framework to support constrained performance optimization of cloud applications using a hybrid approach of artificial neural networks and genetic algorithms. The proposed framework monitors the source system to identify the optimal configurations that maximize application performance based on genuine workload executions. As a result, the experimental analysis presents a novel dataset collected from TPC-C runs on MySQL server. The optimization process is being constrained to satisfy user and application requirements including the cloud infrastructure expenditures, the cost-performance ratio, the baseline performance and the average percentage of idle CPU resources. Furthermore, SONA uses a cloned containerized environment that replicates the main application to avoid system overhead during the optimization process. Our results demonstrate the effectiveness of SONA framework to optimize the performance of OLTP applications deployed on cloud.