Michael Reichert

Autonomic tuning expert: a framework for best-practice oriented autonomic database tuning

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 systems tuning performance under changing workload.

Improving DB2 performance expert – a generic analysis framework

The complexity of software has been dramatically increasing over the years. Database management systems have not escaped this complexity. On the contrary, this problem is aggravated in database systems because they try to integrate multiple paradigms (object, relational, XML) in one box and are supposed to perform well in every scenario unlike OLAP or OLTP. As a result, it is very difficult to fine tune the performance of a DBMS. Hence, there is a need for a external tool which can monitor and fine tune the DBMS. In this extended abstract, we describe a few techniques to improve DB2 Performance Expert, which helps in monitoring DB2. Specifically, we describe a component which is capable of doing early performance problem detection by analyzing the sensor values over a long period of time. We also showcase a trends plotter and workload characterizer which allows a DBA to have a better understanding of the resource usages. A prototype of these tools has been demonstrated to a few select customers and based on their feedback this paper outlines the various issues that still need to be addressed in the next versions of the tool.