An architecture framework for application-managed scaling of cloud-hosted relational databases

Abstract

Scaling relational database in the cloud is one of the critical factors in the migration of applications to the cloud. It is important that applications can directly monitor fine-grained scaling performance (such as consistency-related replication delays and query-specific response time) and specify application-specific policies for autonomic management of the scaling. However, there is no general mechanism and reusable framework and infrastructures to help this. The current facilities in cloud-hosted relational databases are also very limited in providing fine-grained and consumer-centric monitoring data. The situation is exacerbated by the complexity of the different underlying cloud technologies and the need to separate scaling policy from business logic. This paper presents an architecture framework to facilitate a consumer-centric, application-managed autonomic scaling of relational databases in cloud. The architecture framework includes a new consumer-centric monitoring infrastructure and customisable components for sensing, monitoring, analysing and actuation according to application-level scaling policies without modifying an existing application. We evaluated our framework using a modified Web 2.0 application benchmark. The results demonstrate the framework's ability to provide application-level flexibility in achieving improved throughput, data freshness (different levels of consistency) and monetary saving.