Kaa: Evaluating Elasticity of Cloud-Hosted DBMS

Auto-scaling is able to change the scale of an application at runtime. Understanding the application characteristics, scaling impact as well as the workload, an auto-scaler aligns the acquired resources to match the current workload. For distributed Database Management Systems (DBMS) forming the backend of many large-scale cloud applications, it is currently an open question to what extent they support scaling at run-time. In particular, elasticity properties of existing distributed DBMS are widely unknown and difficult to evaluate and compare. This paper presents a comprehensive methodology for the evaluation of the elasticity of distributed DBMS. On the basis of this methodology, we introduce a framework that automates the full evaluation process. We validate the framework by defining significant elasticity scenarios for a case study that comprises two DBMS for write-heavy and read-heavy workloads of different intensities. The results show that scalable distributed DBMS are not necessarily elastic and that adding more instances to a cluster at run-time may even decrease the experienced performance.