LABAREDA: A Predictive and Elastic Load Balancing Service for Cloud-Replicated Databases

Abstract

Cloud computing emerges as an alternative to promote quality of service for data-driven applications. Database management systems must be available to support the deployment of cloud applications resorting to databases. Many solutions use database replication as a strategy to increase availability and decentralize the workload of database transactions among replicas. Due to the distribution of database transactions among replicas, load balancing techniques improve the computational resources utilization. However, several solutions use the current state of the database service to make decisions for the distribution of transactions. This article proposes a predictive and elastic load balancing service for replicated cloud databases. Experiments carried out showed that the use of prediction models can help to predict possible SLA violations presented in time series that represent workloads of cloud-replicated databases.