Abstract
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 analyse which information is required in the system model to support the prediction of the overall DBS behaviour 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 modelling approach which supports the evolutionary refinement of models. We also show how the system model can be used to predict whether or not business goal definitions like the response time will be met.
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Weikum, G., et al.: Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering. In: Bernstein, P.A., et al. (eds.) Proc. of the 28th Intl. Conf. on Very Large Data Bases, pp. 20–31. Morgan Kaufmann, San Francisco (2002)
Weilkiens, T.: Systems Engineering with SysML/UML, 1st edn. Morgan Kaufmann, San Francisco (2008)
Object Management Group: Systems Modeling Language. 1.1 edn. (2008)
Coello, C., et al.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, Heidelberg (2007)
Storm, A.J., et al.: Adaptive Self-Tuning Memory in DB2. In: Dayal, U., et al. (eds.) Proc. of the 32nd Intl. Conf. on Very Large Data Bases, pp. 1081–1092. ACM Press, New York (2006)
Bruno, N., Chaudhuri, S.: An Online Approach to Physical Design Tuning. In: Proc. of the 23rd Intl. Conf. on Data Engineering, pp. 826–835. IEEE Computer Society Press, Los Alamitos (2007)
Krompass, S., et al.: Quality of Service-enabled Management of Database Workloads. IEEE Data Eng. Bull. 31(1), 20–27 (2008)
Niu, B., et al.: Workload adaptation in autonomic DBMSs. In: Erdogmus, H., et al. (eds.) Proc. of the, Conf. of the Center for Advanced Studies on Collaborative Research, p. 13. IBM Press (2006)
Tran, D.N., et al.: A new approach to dynamic self-tuning of database buffers. ACM Transactions on Storage 4(1), 1–25 (2008)
Chung, J.Y., et al.: Goal-oriented dynamic buffer pool management for database systems. In: Proc. of the 1st Intl. Conf. on Engineering of Complex Systems, pp. 191–198. IEEE Computer Society Press, Los Alamitos (1995)
Brown, K.P., et al.: Goal-Oriented Buffer Management Revisited. In: Jagadish, H.V., Mumick, I.S. (eds.) Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, pp. 353–364. ACM Press, New York (1996)
Distributed Management Task Force: Common Information Model (CIM) Infrastructure. 2.5.0a edn, Specification (2008)
IBM Corporation: A Practical Guide to the IBM Autonomic Computing Toolkit. 1st edn., Redbook (2004)
Liu, H., Parashar, M.: Accord: a programming framework for autonomic applications. IEEE Trans. on Systems, Man, and Cybernetics 36(3), 341–352 (2006)
Kumar, V., et al.: iManage: Policy-Driven Self-management for Enterprise-Scale Systems. In: Cerqueira, R., Campbell, R.H. (eds.) Middleware 2007. LNCS, vol. 4834, pp. 287–307. Springer, Heidelberg (2007)
Bhide, M.: et al.: Policy Framework for Autonomic Data Management. In: Proc. of the 1st Intl. Conf. on Autonomic Computing, pp. 336–337. IEEE CS Press, Los Alamitos (2004)
Bhat, V.: et al.: Enabling Self-Managing Applications using Model-based Online Control Strategies. In: Proc. of the 3rd Intl. Conf. on Autonomic Computing, pp. 15–24. IEEE Computer Society Press, Los Alamitos (2006)
Wada, H., et al.: Multiobjective Optimization of SLA-aware Service Composition. In: Proc. of the IEEE Congress on Services - Part I, pp. 368–375. IEEE CS Press, Los Alamitos (2008)
Chang, W.C., et al.: Optimizing Dynamic Web Service Component Composition by Using Evolutionary Algorithms. In: Skowron, A., et al. (eds.) Proc. of the IEEE/WIC/ACM Intl. Conf. on Web Intelligence, pp. 708–711. IEEE CS Press, Los Alamitos (2005)
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Holze, M., Ritter, N. (2009). System Models for Goal-Driven Self-management in Autonomic Databases. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_11
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DOI: https://doi.org/10.1007/978-3-642-04592-9_11
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