Poster Session: Adapting Mixed Workloads to Meet SLOs in Autonomic DBMSs

Authors:
Baoning Niu;Patrick Martin;Wendy Powley;Paul Bird;Randy Horman
Affiliations:
School of Computing, Queen's University, Kingston, Ontario, Canada, K7L 3N6. niu@cs.queensu.ca;School of Computing, Queen's University, Kingston, Ontario, Canada, K7L 3N6. martin@cs.queensu.ca;School of Computing, Queen's University, Kingston, Ontario, Canada, K7L 3N6. wendy@cs.queensu.ca;IBM Toronto Lab, 8200 Warden Ave, Markham, Ontario, L6G 1C7. pbird@ca.ibm.com;IBM Toronto Lab, 8200 Warden Ave, Markham, Ontario, L6G 1C7. horman@ca.ibm.com
Venue:
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Year:
2007

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Abstract

Workload adaptation allows an autonomic database management system (DBMS) to efficiently make use of its resources and meet its Service Level Objectives (SLOs) by filtering or controlling the workload presented to it. Workload adaptation has been shown to be effective for OLAP and OLTP workloads. We outline a framework of workload adaptation and explain how it can be extended to manage mixed workloads comprised of both OLAP and OLTP queries. Experiments with IBM® DB2® Universal Database are presented that illustrate the effectiveness of our techniques.