Towards Autonomic Workload Management in DBMSs

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Author(s): Baoning Niu (Taiyuan University of Technology, China and Queen’s University, Canada), Patrick Martin (Queen’s University, Canada)and Wendy Powley (Queen’s University, Canada)
Copyright: 2009
Volume: 20
Issue: 3
Pages: 17
Source title: Journal of Database Management (JDM)
Editor(s)-in-Chief: Keng Siau (City University of Hong Kong, Hong Kong SAR)
DOI: 10.4018/jdm.2009070101

Keywords: Data Mining and Databases / Databases / Information Science Reference / Library & Information Science

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Abstract

Workload management is the discipline of effectively managing, controlling, and monitoring work flow across computing systems. It is an increasingly important requirement of database management systems (DBMSs) in view of the trends towards server consolidation and more diverse workloads. Workload management is necessary so the DBMS can be business-objective oriented, can provide efficient differentiated service at fine granularity, and can maintain high utilization of resources with low management costs. The authors see that workload management is shifting from offline planning to online adaptation. In this article, the authors discuss the objectives of workload management in autonomic DBMSs and provide a framework for examining how current workload management mechanisms match up with these objectives. They then use the framework to study several mechanisms from both DBMS products and research efforts. They also propose directions for future work in the area of workload management for autonomic DBMSs.

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