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DBMS workload control using throttling: experimental insights
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Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds table of contents
Ontario, Canada
SESSION: Databases table of contents
Article No. 1  
Year of Publication: 2008
Authors
Wendy Powley  Queen's University, Kingston ON
Pat Martin  Queen's University, Kingston ON
Paul Bird  IBM Labs, Toronto, ON
Sponsors
: IBM Toronto Software Lab
: IBM Centers for Advanced Studies (CAS)
Publisher
ACM  New York, NY, USA
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ABSTRACT

Today's database management systems (DBMSs) are required to handle diverse, mixed workloads and to provide differentiated levels of service to ensure that critical work takes priority. In order to meet these needs, it is necessary for a DBMS to have control over the workload executing in the system. Lower priority workloads should be limited to allow higher priority workloads to complete in a timely fashion. In this paper we examine query throttling techniques as a method of workload control. In our approach, a workload class may be slowed down during execution in order to release system resources that can be used by higher priority workloads. We examine two methods of throttling; constant throttling throughout query execution, and a single interruption in which a query is paused for a period of time. A set of experiments using Postresql 8.1 provides insights regarding the performance of these different throttling techniques under different workload conditions and how they compare to using operating system process priority control as a throttling mechanism.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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B. Baryshnikov, C. Clinciu, C. Cunningham, L. Giakoumakis, S. Oks, and S. Stefani. "Managing Query Compilation Memory Consumption to Improve DBMS Throughput", 3rd Biennial Conference on Innovative Data Systems Research (CIDR), January 7--10, 2007, Asilomar, California, USA
 
2
D. P. Brown, A. Richards, R. Zeehandelaar, and D. Galeazzi. "Teradata Active System Management", http://www.teradata.com/t/page/145613/index.html.
 
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IBM Corporation. DB2 Query Patroller Guide: Installation, Administration, and Usage, 2003.
 
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CYGWIN http://www.cygwin.com/.
 
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C. Lang, S. Padmanabhan and K. Wong. "Increasing Buffer-Locality for Multiple Relational Table Scans through Grouping and Throttling", Proceedings of the 23rd International Conference on Data Engineering (ICDE), Istanbul, Turkey, April 17--20, 2007.
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S. Parekh, K. Rose, J. Hellerstein, S. Lightstone, M. Huras and V. Chang, "Managing the Performance Impact of Administrative Utilities", in Self Managing Distributed Systems, Springer Berlin, Heidelberg, February 19, 2004, pp. 130--142.
 
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Transaction Processing Performance Council, TPC-H Specifications, http://www.tpc.org.

Collaborative Colleagues:
Wendy Powley: colleagues
Pat Martin: colleagues
Paul Bird: colleagues