Workload adaptation in autonomic DBMSs

Niu, Baoning; Martin, Patrick; Powley, Wendy; Horman, Randy; Bird, Paul

doi:10.1145/1188966.1188984

Cited by 29 publications

(20 citation statements)

References 6 publications

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“…A framework for workload adaptation by Niu et al (2006) is proposed that has two components, i.e. workload detection and workload control.…”

Section: Critical Discussionmentioning

confidence: 99%

“…ASM maps the performance class with allocation group and has not ability to predict the resource demand for each performance class. The experiment in Query Scheduler Niu et al (2006) is performed on the stable workload, which is not suitable for dynamic environment where the workload changes rapidly. During the experiments, total cost of a query instead of detailed cost is used as a parameter that may generates wrong results.…”

Section: Critical Discussionmentioning

confidence: 99%

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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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“…A framework for workload adaptation by Niu et al (2006) is proposed that has two components, i.e. workload detection and workload control.…”

Section: Critical Discussionmentioning

confidence: 99%

Section: Critical Discussionmentioning

confidence: 99%

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

et al. 2012

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“…In this section, we describe a query scheduler that uses query optimizer cost estimates, and we experimentally compare against this scheduler in Section 9. The scheduler that uses query optimizer cost estimates is based on the work of Niu et al [33,34,48,49] (in particular, the query scheduler described in detail in [33,48]). The query scheduler in these works uses the query optimizer cost estimates (termed as timerons in IBM DB2) to measure the cost of the queries executing in the system.…”

Section: Scheduling Based On Query Optimizer Cost Estimatesmentioning

confidence: 99%

“…The work in [33,34,48,49] also includes a general framework for workload adaptation that can handle scheduling for time-varying workloads. In addition, the different service classes can have different service level objectives, and the scheduler dynamically adjusts the timeron threshold for each service class based on a utility function that quantifies how well the different service level classes presently meet their service level objectives.…”

Section: Scheduling Based On Query Optimizer Cost Estimatesmentioning

confidence: 99%

Interaction-aware scheduling of report-generation workloads

et al. 2011

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The typical workload in a database system consists of a mix of multiple queries of different types that run concurrently. Interactions among the different queries in a query mix can have a significant impact on database performance. Hence, optimizing database performance requires reasoning about query mixes rather than considering queries individually. Current database systems lack the ability to do such reasoning. We propose a new approach based on planning experiments and statistical modeling to capture the impact of query interactions. Our approach requires no prior assumptions about the internal workings of the database system or the nature and cause of query interactions; making it portable across systems.To demonstrate the potential of modeling and exploiting query interactions, we have developed a novel interactionaware query scheduler for report-generation workloads. Our scheduler, called QShuffler, uses two query scheduling algorithms that leverage models of query interactions. The first algorithm is optimized for workloads where queries are submitted in large batches. The second algorithm targets workloads where queries arrive continuously, and scheduling decisions have to be made on-line. We report an experimental evaluation of QShuffler using TPC-H workloads running on IBM DB2. The evaluation shows that QShuffler, by modeling and exploiting query interactions, can consistently out-

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“…The QoS requirements of high importance workloads are always satisfied before that of low importance workloads. Pacifici et al [9] and Niu et al [6] use an approach in which a system objective function, which is the sum of workload utilities, is maximized to find an optimal resource allocation plan to meet the performance goals of workloads. The importance of workloads is enforced through assigning different utility functions.…”

Section: Related Workmentioning

confidence: 99%

Managing Workload Importance in Enterprise DBMSs

Niu

Xue

Martin

et al. 2010

2010 IEEE 12th Conference on Commerce and Enterprise Computing

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One of the desirable properties of enterprise DBMSs is the ability to manage workloads based on their importance to business. This calls for mechanisms that allow enterprise DBMSs to be able to translate the workload importance into resource allocation plans, which means workload importance must be quantitatively evaluated in order to play its role when enterprise DBMSs allocate system resources to workloads. This paper introduces a framework for implementing the translation and a process for quantifying workload importance. Under the framework, workload importance is encapsulated into an objective function that is optimized to derive resource allocation plans. A general form of workload utility functions is developed for simplifying the job of quantifying workload importance. Experiments show their effectiveness.

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Workload adaptation in autonomic DBMSs

Cited by 29 publications

References 6 publications

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

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

Interaction-aware scheduling of report-generation workloads

Managing Workload Importance in Enterprise DBMSs

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