Toward autonomic computing with DB2 universal database

Lightstone, Sam; Lohman, Guy M.; Zilio, Daniel

doi:10.1145/601858.601873

Cited by 38 publications

(18 citation statements)

References 20 publications

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“…This adaptive behavior of Multi-Query PI shows the consistency with the trends of automatic and AC. Query Patroller, IBM Corporation (2003); Lightstone et al (2002) monitors the given workload; perform analysis and prioritize it schedules for the incoming requests from the users. It limits the flow of long running queries to avoid saturation and ensures better resource utilization on the basis of profile (created by the administrator).…”

Section: Critical Discussionmentioning

confidence: 99%

Workload management: a technology perspective with respect to self-* characteristics

et al. 2012

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Rapid growth in data, maximum functionality requirements and changing behavior in the database workload tends the workload management to be more complex. Organizations have complex type of workloads that are very difficult to manage by humans and even in some cases this management becomes impossible. Human experts take long time to get sufficient experience so that they can manage the workload efficiently. The versatility in workload due to huge data size and user requirements leads us towards the new challenges. One of the challenges is the identification of the problematic queries and the decision about these, i.e. whether to continue their execution or stop. The other challenge is how to characterize the workload, as the tasks such as configuration, prediction and adoption are fully dependent on the workload characterization. Correct and timely characterization leads managing the workload in an efficient manner and vice versa. In this scenario, our objective is to produce a workload management strategy or framework that is fully adoptive. The paper provides a summary of the structure and achievements of the database tools that exhibit Autonomic Computing or self-* characteristics in workload management. We have categorized the database workload tools to these self-* characteristics and identified their limitations. Finally the paper presents the research done in the database workload management tools with respect to the workload type and Autonomic Computing.

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Section: Critical Discussionmentioning

confidence: 99%

Workload management: a technology perspective with respect to self-* characteristics

et al. 2012

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“…Quite simply, it is about freeing IT professionals to focus on higher-value tasks by making technology work smarter, with business rules guiding systems to be self-configuring, self-healing, self-optimizing and self-protecting [6]. This subject is of great interest to enterprises and has already been put into practice for improving database performance by IBM [19], [15] and Microsoft [2]. There is great interest of development into applications of autonomic computing on managing data warehouses, as experts can no longer face the quantity of information available IBM specifications link autonomic computing with the notion of autonomic manager as the entity that coordinates the activity of the autonomic process.…”

Section: Autonomic Computing In Data Warehouse Managementmentioning

confidence: 99%

An Ontology-Based Autonomic System for Improving Data Warehouse Performances

Nicolicin-Georgescu

Benatier

Lehn

et al. 2009

Knowledge-Based and Intelligent Information and Engineering Systems

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“…For self-optimization, a system may dynamically update the parameters used by cost functions or automatically reorganize indexes to reduce fragmentation (e.g. [14,15]). For self-protection, a system may dynamically limit the resources provided to a long-running query to reduce the impact of any one request on others.…”

Section: Database Administrationmentioning

confidence: 99%

Autonomics and data management

Paton

2008

Concurrency and Computation

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SUMMARYTraditionally, database management systems have been associated with high-cost, high-quality functionalities. That is, powerful capabilities are provided, but only in response to careful design, procurement, deployment and administration. This has been successful in many contexts, but in an environment in which data are available in increasing quantities under the management of a growing collection of distributed applications, and where effective use of available data often provides a competitive edge, there is a requirement for various benefits of a comprehensive data management infrastructure to be made available with rather fewer of the costs. If this requirement is to be met, automation will need to be deployed much more widely and systematically in data management platforms. This paper reviews recent results on autonomic data management, makes a case that current practice presents significant opportunities for further development and argues that comprehensive support for automation should be central to future data management infrastructures, whether centralized or distributed.

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Toward autonomic computing with DB2 universal database

Cited by 38 publications

References 20 publications

Workload management: a technology perspective with respect to self-* characteristics

Workload management: a technology perspective with respect to self-* characteristics

An Ontology-Based Autonomic System for Improving Data Warehouse Performances

Autonomics and data management

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