Estimating the resource demand of a highly configurable software system like an in-memory database is a difficult task. Many factors such as the workload, flexible resource allocation, multi-tenancy and various configuration settings influence the actual performance behavior of such systems. Cloud providers offering Database-as-a-Service applications need to monitor and apply these factors in order to utilize their systems in an efficient and cost-effective manner. However, only observing the CPU utilization of the database’s processes, as done by traditional performance approaches, is not sufficient to accomplish this task. This is especially relevant for environments with multiple active database tenants, which adds another level of complexity to the thread handling on multiple layers like the database management system or the operating system. In this paper, we propose a fine-grained monitoring setup allowing us to analyze the performance of virtualized multi-tenant databases. Our focus is on extensively collecting and analyzing performance data on a thread level. We utilize this setup to show the performance influence of varying database configuration settings, different workload characteristics, multi-tenancy and virtualization features. Therefore, we conducted several experiments by applying the TPC-H benchmark generating OLAP workload on a SAP HANA database in a virtualized environment. In our experiments, we can show a strong dependency between the specific type of workload and the performance. Furthermore, we analyze the workload-dependent performance improvements and the performance degradation when changing the runtime configuration.     «

Estimating the resource demand of a highly configurable software system like an in-memory database is a difficult task. Many factors such as the workload, flexible resource allocation, multi-tenancy and various configuration settings influence the actual performance behavior of such systems. Cloud providers offering Database-as-a-Service applications need to monitor and apply these factors in order to utilize their systems in an efficient and cost-effective manner. However, only observing the CP...     »