增量计算与近似计算的结合
The Marriage of Incremental and Approximate Computing
摘要：
大多数需要低延迟执行和有效利用计算资源的数据分析系统越来越多地采用两种计算范式，即增量计算和近似计算。增量计算以增量方式更新输出，而不是在输入发生变化的情况下连续运行作业时从头开始重新计算所有内容。近似计算返回作业的近似输出，而不是精确输出。这两种范式都依赖于对数据项的子集进行计算，而不是对整个数据集进行计算，但它们在跳过部分计算的方式上有所不同。增量计算依赖于子计算的中间结果的记忆，并且跨作业重用这些记忆的结果用于不受改变的输入影响的子计算。近似计算依赖于对整个数据集的代表性采样来计算数据项的子集。在这篇论文中，我们观察到这两种计算范式是互补的，可以结合在一起！高层次的想法是：设计一个采样算法，使样本选择偏向于以前运行的记忆数据项。为了具体化这个想法，我们设计了一个在线分层抽样算法，使用自调整计算产生一个增量更新的近似输出有界误差。我们在一个名为IncAppox的基于Apache Spark Streaming的数据分析系统中实现了我们的算法。我们对系统的评估表明，IncApprox实现了增量和近似计算的好处。
英文摘要：
Most data analytics systems that require low-latency execution and efficient utilization of computing resources, increasingly adopt two computational paradigms, namely, incremental and approximate computing. Incremental computation updates the output incrementally instead of re-computing everything from scratch for successive runs of a job with input changes. Approximate computation returns an approximate output for a job instead of the exact output. Both paradigms rely on computing over a subset of data items instead of computing over the entire dataset, but they differ in their means for skipping parts of the computation. Incremental computing relies on the memoization of intermediate results of sub-computations, and reusing these memoized results across jobs for sub-computations that are unaffected by the changed input. Approximate computing relies on representative sampling of the entire dataset to compute over a subset of data items. In this thesis, we make the observation that these two computing paradigms are complementary, and can be married together! The high level idea is to: design a sampling algorithm that biases the sample selection to the memoized data items from previous runs. To concretize this idea, we designed an online stratified sampling algorithm that uses self-adjusting computation to produce an incrementally updated approximate output with bounded error. We implemented our algorithm in a data analytics system called IncAppox based on Apache Spark Streaming. Our evaluation of the system shows that IncApprox achieves the benefits of both incremental and approximate computing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
