Autonomic tuning expert: a framework for best-practice oriented autonomic database tuning
Autonomic tuning expert: a framework for best-practice oriented autonomic database tuning
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DOI：
10.1145/1463788.1463792
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发表时间：
2008-10
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影响因子：
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通讯作者：
D. Wiese;G. Rabinovitch;Michael Reichert;Stephan Arenswald
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文献类型：
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作者：
D. Wiese;G. Rabinovitch;Michael Reichert;Stephan Arenswald
数据库的规模和复杂性正在迅速增长。在任何情况下都需要满足高性能、可用性和进一步的服务级别协议，以取悦客户。为了在复杂的环境中调优DBMS，需要高技能的数据库管理员（DBA）。不幸的是，它们正变得越来越稀有，越来越昂贵。改进性能分析并实现大型信息管理平台的自动化需要更直观、更灵活的决策来源。本文指出了最佳实践知识对自主数据库调优的重要性，并提出了形式化和存储DBA专家调优知识的想法。我们将把注意力集中在为IBM DB2开发面向最佳实践的自主数据库调优参考系统上，并随后评估我们的系统在不断变化的工作负载下的调优性能。
Databases are growing rapidly in scale and complexity. High performance, availability, and further service level agreements need to be satisfied under any circumstances to please customers. In order to tune the DBMS within their complex environments, highly skilled database administrators (DBAs) are required. Unfortunately, they are becoming rarer and more and more expensive. Improving performance analysis and moving towards the automation of large information management platforms requires a more intuitive and flexible source of decision making. This paper points out the importance of best-practices knowledge for autonomic database tuning and addresses the idea of formalizing and storing DBA expert tuning knowledge for the autonomic management process. We will focus our attention on the development of a reference system for best-practice oriented autonomic database tuning for IBM DB2 and subsequently evaluate our system's tuning performance under changing workload.