Goal-oriented buffer management revisited
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Goal-oriented dynamic buffer pool management for data base systems
ICECCS '95 Proceedings of the 1st International Conference on Engineering of Complex Computer Systems
The dawning of the autonomic computing era
IBM Systems Journal
Policy Framework for Autonomic Data Management
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Optimizing Dynamic Web Service Component Composition by Using Evolutionary Algorithms
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Foundations of Automated Database Tuning
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Adaptive self-tuning memory in DB2
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Workload adaptation in autonomic DBMSs
CASCON '06 Proceedings of the 2006 conference of the Center for Advanced Studies on Collaborative research
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A new approach to dynamic self-tuning of database buffers
ACM Transactions on Storage (TOS)
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
Multiobjective Optimization of SLA-Aware Service Composition
SERVICES '08 Proceedings of the 2008 IEEE Congress on Services - Part I
Enabling Self-Managing Applications using Model-based Online Control Strategies
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Consistent on-line classification of dbs workload events
Proceedings of the 18th ACM conference on Information and knowledge management
Accord: a programming framework for autonomic applications
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Self-managing databases intend to reduce the total cost of ownership for a DBS by automatically adapting the DBS configuration to evolving workloads and environments. However, existing techniques strictly focus on automating one particular administration task, and therefore cause problems like overreaction and interference. To prevent these problems, the self-management logic requires knowledge about the system-wide effects of reconfiguration actions. In this paper we therefore describe an approach for creating a DBS system model, which serves as a knowledge base for DBS self-management solutions. We analyze which information is required in the system model to support the prediction of the overall DBS behavior under different configurations, workloads, and DBS states. As creating a complete quantitative description of existing DBMS in a system model is a difficult task, we propose a modeling approach which supports the evolutionary refinement of models. We also show how the system model can be evaluated to predict whether or not business goal definitions like the response time will be met.