NDLI: Non-linear Optimization of Performance Functions for Autonomic Database Performance Tuning

Content Provider	IEEE Xplore Digital Library
Author	Rabinovitch, G. Wiese, D.
Copyright Year	2007
Description	Author affiliation: Friedrich-Schiller-Univ. Jena, Jena (Rabinovitch, G.; Wiese, D.)
Abstract	Modern ondemand environments are coined by a heterogeneous diversity of components, architectures and applications. High performance, availability and further service level agreements need to be satisfied under any circumstances in order to please customers. Today, highly skilled database administrators (DBAs) are required to tune the DBMS within their complex environments. Achieved DBMS' performance depends on individual DBA skills, home-grown tuning scripts and in most cases is reactive to obvious and urgent performance problems. This paper addresses the idea of classifying, formalizing, obtaining, storing, maintaining, exchanging and individually adapting DBA expert tuning-knowledge as shared domain of understanding in the autonomic management process. Hereby, we focus our attention on the development of a resource dependency model that allows for (precise) optimization and decision-support at run-time, in contrast to traditional trial-and- error, feedback-based tuning methodologies based on best- practices.
Starting Page	48
Ending Page	48
File Size	589023
Page Count	1
File Format	PDF
ISBN	0769528597
DOI	10.1109/CONIELECOMP.2007.89
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-19
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	resource dependencies tuning knowledge Optimization methods Relational databases Control systems non-linear regression MAPE Environmental management self-management Information systems Runtime performance Autonomic nervous system optimization Management information systems Physiology Protection autonomic database tuning problem resolution
Content Type	Text
Resource Type	Article

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