Measuring and Managing Answer Quality for Online Data-Intensive Services

2015 IEEE International Conference on Autonomic Computing Pub Date : 2015-06-17 DOI:10.1109/ICAC.2015.33

Jaimie Kelley, Christopher Stewart, Nathaniel Morris, Devesh Tiwari, Yuxiong He, S. Elnikety

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引用次数: 33

Abstract

Online data-intensive services parallelize query execution across distributed software components. Interactive response time is a priority, so online query executions return answers without waiting for slow running components to finish. However, data from these slow components could lead to better answers. We propose Ubora, an approach to measure the effect of slow running components on the quality of answers. Ubora randomly samples online queries and executes them twice. The first execution elides data from slow components and provides fast online answers, the second execution waits for all components to complete. Ubora uses memoization to speed up mature executions by replaying network messages exchanged between components. Our systems-level implementation works for a wide range of platforms, including Hadoop/Yarn, Apache Lucene, the Easy Rec Recommendation Engine, and the Open Ephyra question answering system. Ubora computes answer quality much faster than competing approaches that do not use memoization. With Ubora, we show that answer quality can and should be used to guide online admission control. Our adaptive controller processed 37% more queries than a competing controller guided by the rate of timeouts.

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在线数据密集型服务的回答质量测量与管理

在线数据密集型服务可以跨分布式软件组件并行执行查询。交互响应时间是一个优先级，因此在线查询执行返回答案，而无需等待运行缓慢的组件完成。然而，来自这些慢速组件的数据可能会带来更好的答案。我们提出了Ubora，一种测量慢速运行组件对答案质量影响的方法。Ubora随机抽样在线查询并执行两次。第一次执行删除来自慢速组件的数据并提供快速的在线答案，第二次执行等待所有组件完成。Ubora通过重放组件之间交换的网络消息来加速成熟的执行。我们的系统级实现适用于广泛的平台，包括Hadoop/Yarn、Apache Lucene、Easy Rec推荐引擎和Open Ephyra问答系统。Ubora计算答案质量的速度比不使用记忆的竞争方法快得多。通过Ubora，我们证明了答案质量可以而且应该用来指导在线录取控制。我们的自适应控制器处理的查询比由超时率引导的竞争控制器多37%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 IEEE International Conference on Autonomic Computing

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