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Inhaltsangabe
The knowledge of the system, the data stored, the workload and the inter-dependency between them is a major requirement for tuning a Database Management System (DBMS). Due to complexity of the DBMSs and the diversity of their workload, there is a need for automatic tuning of DBMS. Self-managing (or autonomic) databases are intended to reduce the total cost of ownership by automatically adapting to evolving workloads and environments. To reach this goal, commercial DBMSs have recently been equipped with self-management functions, which support the database administrator (DBA) in identifying the appropriate indexes or in sizing the memory areas. However, existing techniques suffer from several problems: First, they are often implemented as off-line tools that have to be explicitly triggered by a DBA. Second, they strictly focus on automating one particular administration task, without considering possible side-effects on other components. This book defines the automated manner to make the system self tune in variable workload.
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Reseña del editor
The knowledge of the system, the data stored, the workload and the inter-dependency between them is a major requirement for tuning a Database Management System (DBMS). Due to complexity of the DBMSs and the diversity of their workload, there is a need for automatic tuning of DBMS. Self-managing (or autonomic) databases are intended to reduce the total cost of ownership by automatically adapting to evolving workloads and environments. To reach this goal, commercial DBMSs have recently been equipped with self-management functions, which support the database administrator (DBA) in identifying the appropriate indexes or in sizing the memory areas. However, existing techniques suffer from several problems: First, they are often implemented as off-line tools that have to be explicitly triggered by a DBA. Second, they strictly focus on automating one particular administration task, without considering possible side-effects on other components. This book defines the automated manner to make the system self tune in variable workload.
Biografía del autor
Dr. Hitesh Kumar Sharma is an Assistant Professor (Senior Scale) in Dept. of CSE, University of Petroleum & Energy Studies, Dehradun, He has conducted various National Workshops and National/ International Conferences. He has more than 40 Research publications. Currently he is working in Department of Analytics under the umbrella of Dept. of CSE.
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Automated Database Tuning using Dynamic SGA Parameters
Verlag:
LAP LAMBERT Academic Publishing Mai 2017, 2017
ISBN 10: 365975112X
ISBN 13: 9783659751127
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Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: Neu. Neuware -The knowledge of the system, the data stored, the workload and the inter-dependency between them is a major requirement for tuning a Database Management System (DBMS). Due to complexity of the DBMSs and the diversity of their workload, there is a need for automatic tuning of DBMS. Self-managing (or autonomic) databases are intended to reduce the total cost of ownership by automatically adapting to evolving workloads and environments. To reach this goal, commercial DBMSs have recently been equipped with self-management functions, which support the database administrator (DBA) in identifying the appropriate indexes or in sizing the memory areas. However, existing techniques suffer from several problems: First, they are often implemented as off-line tools that have to be explicitly triggered by a DBA. Second, they strictly focus on automating one particular administration task, without considering possible side-effects on other components. This book defines the automated manner to make the system self tune in variable workload.Books on Demand GmbH, Überseering 33, 22297 Hamburg 184 pp. Englisch. Artikel-Nr. 9783659751127
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Automated Database Tuning using Dynamic SGA Parameters: A Practical Approach
Verlag:
LAP LAMBERT Academic Publishing, 2017
ISBN 10: 365975112X
ISBN 13: 9783659751127
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Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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Paperback. Zustand: Brand New. 184 pages. 8.66x5.91x0.42 inches. In Stock. Artikel-Nr. 365975112X
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