On Workload Characterization of Relational Database Environments
IEEE Transactions on Software Engineering
Today's DBMSs: How autonomic are they?
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
The dawning of the autonomic computing era
IBM Systems Journal
Self-Managing Systems: A Control Theory Foundation
ECBS '05 Proceedings of the 12th IEEE International Conference and Workshops on Engineering of Computer-Based Systems
Recommending Materialized Views and Indexes with IBM DB2 Design Advisor
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Automatic physical design tuning: workload as a sequence
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Workload Models for Autonomic Database Management Systems
ICAS '06 Proceedings of the International Conference on Autonomic and Autonomous Systems
To tune or not to tune?: a lightweight physical design alerter
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Adaptive self-tuning memory in DB2
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Automatic SQL tuning in oracle 10g
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Automated statistics collection in DB2 UDB
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
On-Line Index Selection for Shifting Workloads
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Autonomic Databases: Detection of Workload Shifts with n-Gram-Models
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
On predictive modeling for optimizing transaction execution in parallel OLTP systems
Proceedings of the VLDB Endowment
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Due to the complexity of industry-scale database systems, the total cost of ownership for these systems is no longer dominated by hardware and software, but by administration expenses. Autonomic databases intend to reduce these costs by providing self-management features. Existing approaches towards this goal are supportive advisors for the database administrator and feedback control loops for online monitoring, analysis and re-configuration. But while advisors are too resource-consuming for continuous operation, feedback control loops suffer from overreaction, oscillation and interference. In this position paper we give a general analysis of the parameters that affect the self-management of a database. Out of these parameters, we identify that the workload has major influence on both physical design of data and DBMS configuration. Hence, we propose to employ a workload model for light-weight, continuous workload monitoring and analysis. This model can be used for the identification and prediction of significant workload shifts, which require autonomic re-configuration of the database.