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Utility-driven adaptive query workload execution

机译:实用程序驱动的自适应查询工作负载执行

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Workload management coordinates access to and use of shared computational resources; adaptive workload execution revises resource allocation decisions dynamically in response to feedback about the progress of the workload or the behavior of the resources. Where the workload contains or consists of database queries, adaptive query processing (AQP) changes the way in which a query is being evaluated while the query is running. In parallel environments, available adaptations may change the allocation of query fragments to a machine, for example to remove load imbalance or change the parallelism level. Most AQP strategies act on individual queries with the objective of reducing response times. However, where adaptations affect the usage of shared resources, or the principal goal is to meet quality of service targets rather than to minimize overall response times, locally beneficial decisions may have globally detrimental effects. This paper describes the use of utility functions to coordinate adaptations that assign resources to query fragments from multiple queries, and demonstrates how a common framework can be used to support different objectives, specifically to minimize overall query response times and to maximize the number of queries meeting quality of service goals. Experiments using simulation compare the use of utility functions with the more common heuristic control strategies, demonstrating situations in which significant benefits can be obtained.
机译:工作负载管理协调对共享计算资源的访问和使用;自适应工作负载执行响应于有关工作负载进度或资源行为的反馈而动态修改资源分配决策。当工作负载包含数据库查询或由数据库查询组成时,自适应查询处理(AQP)会更改查询运行时评估查询的方式。在并行环境中,可用的适应方法可能会更改查询片段对计算机的分配,例如以消除负载不平衡或更改并行度。大多数AQP策略都会针对单个查询采取行动,以减少响应时间。但是,如果适应措施影响共享资源的使用,或者主要目标是达到服务质量目标而不是最小化总体响应时间,则对本地有利的决策可能会对全球产生不利影响。本文介绍了使用实用程序功能来协调为多个查询的查询片段分配资源的适应性,并演示了如何使用通用框架来支持不同的目标,特别是最大程度地减少了总体查询响应时间并最大程度地满足了查询数量服务质量目标。使用模拟进行的实验将效用函数的使用与更常见的启发式控制策略进行了比较,证明了可以获得显着收益的情况。

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