No abstract

A question that database administrators (DBAs) routinely need to answer is how long a batch query workload will take to complete. This question arises, for example, while planning the execution of different report-generation workloads to fit within available time windows. To answer this question accurately, we need to take into account that the typical workload in a database system consists of mixes of concurrent queries. Interactions among different queries in these mixes need to be modeled, rather than the conventional approach of considering each query separately. This paper presents a new approach for estimating workload completion times that takes the significant impact of query interactions into account. This approach builds performance models using an experimentdriven technique, by sampling the space of possible query mixes and fitting statistical models to the observed performance at these samples. No prior assumptions are made about the internal workings of the database system or the cause of query interactions, making the models robust and portable. We show that a careful choice of sampling and statistical modeling strategies can result in accurate models, and we present a novel interaction-aware workload simulator that uses these models to estimate workload completion times. An experimental evaluation with complex TPC-H queries on IBM DB2 shows that this approach consistently predicts workload completion times with less than 20% error.

No abstract

Virtual machine monitors are becoming popular tools for the deployment of database management systems and other enterprise software. In this article, we consider a common resource consolidation scenario in which several database management system instances, each running in a separate virtual machine, are sharing a common pool of physical computing resources. We address the problem of optimizing the performance of these database management systems by controlling the configurations of the virtual machines in which they run. These virtual machine configurations determine how the shared physical resources will be allocated to the different database system instances. We introduce a virtualization design advisor that uses information about the anticipated workloads of each of the database systems to recommend workload-specific configurations offline. Furthermore, runtime information collected after the deployment of the recommended configurations can be used to refine the recommendation and to handle changes in the workload. To estimate the effect of a particular resource allocation on workload performance, we use the query optimizer in a new what-if mode. We have implemented our approach using both PostgreSQL and DB2, and we have experimentally evaluated its effectiveness using DSS and OLTP workloads.

Virtual machine monitors are becoming popular tools for the deployment of database management systems and other enterprise software applications. In this paper, we consider a common resource consolidation scenario, in which several database management system instances, each running in a virtual machine, are sharing a common pool of physical computing resources. We address the problem of optimizing the performance of these database management systems by controlling the configurations of the virtual machines in which they run. These virtual machine configurations determine how the shared physical resources will be allocated to the different database instances. We introduce a virtualization design advisor that uses information about the anticipated workloads of each of the database systems to recommend workload-specific configurations offline. Furthermore, runtime information collected after the deployment of the recommended configurations can be used to refine the recommendation. To estimate the effect of a particular resource allocation on workload performance, we use the query optimizer in a new what-if mode. We have implemented our approach using both PostgreSQL and DB2, and we have experimentally evaluated its effectiveness using DSS and OLTP workloads.