Towards workload shift detection and prediction for autonomic databases
Proceedings of the ACM first Ph.D. workshop in CIKM
Compressing Very Large Database Workloads for Continuous Online Index Selection
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
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
Index interactions in physical design tuning: modeling, analysis, and applications
Proceedings of the VLDB Endowment
Semi-automatic index tuning: keeping DBAs in the loop
Proceedings of the VLDB Endowment
Kaizen: a semi-automatic index advisor
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Optimizing index deployment order for evolving OLAP
Proceedings of the 15th International Conference on Extending Database Technology
TileHeat: a framework for tile selection
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Optimizing adaptive multi-route query processing via time-partitioned indices
Journal of Computer and System Sciences
SMIX: self-managing indexes for dynamic workloads
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Proceedings of the 17th International Database Engineering & Applications Symposium
Workload management: a technology perspective with respect to self-* characteristics
Artificial Intelligence Review
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This paper introduces COLT (Continuous On-Line Tuning), a novel framework that continuously monitors the workload of a database system and enriches the existing physical design with a set of effective indices. The key idea behind COLT is to gather performance statistics at different levels of detail and to carefully allocate profiling resources to the most promising candidate configurations. Moreover, COLT uses effective heuristics to self-regulate its own performance, lowering its overhead when the system is well tuned and being more aggressive when the workload shifts and it becomes necessary to re-tune the system. We describe an implementation of the proposed framework in the PostgreSQL database system and evaluate its performance experimentally. Our results validate the effectiveness of COLT and demonstrate its ability to modify the system configuration in response to changes in the query load.