Capacity planning and performance modeling: from mainframes to client-server systems
Capacity planning and performance modeling: from mainframes to client-server systems
Neural, Novel and Hybrid Algorithms for Time Series Prediction
Neural, Novel and Hybrid Algorithms for Time Series Prediction
Benchmark Handbook: For Database and Transaction Processing Systems
Benchmark Handbook: For Database and Transaction Processing Systems
Automatically classifying database workloads
Proceedings of the eleventh international conference on Information and knowledge management
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Automating Statistics Management for Query Optimizers
IEEE Transactions on Knowledge and Data Engineering
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
A methodology for auto-recognizing DBMS workloads
CASCON '02 Proceedings of the 2002 conference of the Centre for Advanced Studies on Collaborative research
DB2 Advisor: An Optimizer Smart Enough to Recommend its own Indexes
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Today's DBMSs: How autonomic are they?
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
Robust query processing through progressive optimization
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
The dawning of the autonomic computing era
IBM Systems Journal
Resource Selection for Autonomic Database Tuning
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
TCON'95 Proceedings of the USENIX 1995 Technical Conference Proceedings
Is it DSS or OLTP: automatically identifying DBMS workloads
Journal of Intelligent Information Systems
Performance Forecasting for Performance Critical Huge Databases
Proceedings of the 2011 conference on Information Modelling and Knowledge Bases XXII
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Self-optimization is one of the defining characteristics of an autonomic computing system. For a complex system, such as the database management system (DBMS), to be self-optimizing it should recognize properties of its workload and be able to adapt to changes in these properties over time. The workload type, for example, is a key to tuning a DBMS and may vary over the system's normal processing cycle. Continually monitoring a DBMS, using a special tool called Workload Classifier, in order to detect changes in the workload type can inevitably impose a significant overhead that may degrade the overall performance of the system. Instead, the DBMS should selectively monitor the workload during some specific periods recommended by the Psychic-Skeptic Prediction (PSP) framework that we introduce in this work. The PSP framework allows the DBMS to forecast major shifts in the workload by combining off-line and on-line prediction methods. We integrate the Workload Classifier with the PSP framework in order to come up with an architecture by which the autonomous DBMS can tune itself efficiently. Our experiments show that this approach is effective and resilient as the prediction framework adapts gracefully to changes in the workload patterns.