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

Authors:
David Wiese;Gennadi Rabinovitch;Michael Reichert;Stephan Arenswald
Affiliations:
Friedrich-Schiller-University, Jena, Germany;Friedrich-Schiller-University, Jena, Germany;IBM Deutschland Research & Development GmbH, Boeblingen, Germany;IBM Deutschland Research & Development GmbH, Boeblingen, Germany
Venue:
CASCON '08 Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds
Year:
2008

Citing 12
Cited 0

Rethinking Database System Architecture: Towards a Self-Tuning RISC-Style Database System

VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Composite Events for Active Databases: Semantics, Contexts and Detection

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Towards Automated Performance Tuning for Complex Workloads

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Toward a new landscape of systems management in an autonomic computing environment

IBM Systems Journal
Design and implementation of the GLIF3 guideline execution engine

Journal of Biomedical Informatics
Automatic Diagnosis of Performance Problems in Database Management Systems

ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Towards workload-aware dbmss: identifying workload type and predicting its change

Towards workload-aware dbmss: identifying workload type and predicting its change
A Reflective Database-Oriented Framework for Autonomic Managers

ICAS '06 Proceedings of the International Conference on Autonomic and Autonomous Systems
Adaptive self-tuning memory in DB2

VLDB '06 Proceedings of the 32nd international conference on Very large data bases
DB2 Udb Ese V8 Non-dpf Performance Guide for High Performance Oltp And Bi (IBM Redbooks)

DB2 Udb Ese V8 Non-dpf Performance Guide for High Performance Oltp And Bi (IBM Redbooks)
Non-linear Optimization of Performance Functions for Autonomic Database Performance Tuning

ICAS '07 Proceedings of the Third International Conference on Autonomic and Autonomous Systems
Self-tuning database technology and information services: from wishful thinking to viable engineering

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases

Quantified Score

Hi-index	0.00

Visualization

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.