Scalable Workload Adaptation for Mixed Workload

Niu, Baoning; Shi, Jian Fang

doi:10.1007/978-3-642-10485-5_9

Cited by 1 publication

(2 citation statements)

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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%

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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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