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

ABSTRACT

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.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.7 Database Administration

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.3 Languages

          Nouns: DB2

General Terms:
Performance