Proceedings Article10.1145/1376616.1376711
Automatic virtual machine configuration for database workloads
Ahmed A. Soror,Umar Farooq Minhas,Ashraf Aboulnaga,Kenneth Salem,Peter Kokosielis,Sunil Kamath +5 more
- 09 Jun 2008
- pp 953-966
125
TL;DR: This paper introduces a virtualization design advisor that uses information about the anticipated workloads of each of the database systems to recommend workload-specific configurations offine, and runtime information collected after the deployment of the recommended configurations can be used to refine the recommendation.
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Abstract: 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 offine. 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.
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Citations
Automatic Database Management System Tuning Through Large-scale Machine Learning
Dana Van Aken,Andrew Pavlo,Geoffrey J. Gordon,Bohan Zhang +3 more
- 09 May 2017
TL;DR: An automated approach that leverages past experience and collects new information to tune DBMS configurations and recommends configurations that are as good as or better than ones generated by existing tools or a human expert is presented.
602
Tuning database configuration parameters with iTuned
Songyun Duan,Vamsidhar Thummala,Shivnath Babu +2 more
- 01 Aug 2009
TL;DR: ITuned is described, a tool that automates the task of identifying good settings for database configuration parameters and has three novel features: a technique called Adaptive Sampling that proactively brings in appropriate data through planned experiments to find high-impact parameters and high-performance parameter settings.
VCONF: a reinforcement learning approach to virtual machines auto-configuration
Jia Rao,Xiangping Bu,Cheng-Zhong Xu,Le Yi Wang,George Yin +4 more
- 15 Jun 2009
TL;DR: A reinforcement learning (RL) based approach, namely VCONF, to automate the VM configuration process, which employs model-based RL algorithms to address the scalability and adaptability issues in applying RL in systems management.
Predicting query execution time: Are optimizer cost models really unusable?
Wentao Wu,Yun Chi,Shenghuo Zhu,Junichi Tatemura,Hakan Hacigumus,J.F. Naughton +5 more
- 08 Apr 2013
TL;DR: This paper investigates the novel idea of spending extra resources to refine estimates for the query plan after it has been chosen by the optimizer but before execution and finds a well calibrated query optimizer model along with cardinality estimation refinement provides a low overhead way to provide estimates that are always competitive.
URL: A unified reinforcement learning approach for autonomic cloud management
TL;DR: A unified reinforcement learning approach, namely URL, to automate the configuration processes of virtualized machines and appliances running in the virtual machines, lends itself to the application of real-time autoconfiguration of clouds.
184
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