Stateful Adaptive Streams with Approximate Computing and Elastic Scaling

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
João Francisco, Miguel E. Coimbra, P. Neto, Felix Freitag, L. Veiga
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引用次数: 0

Abstract

The model of approximate computing can be used to increase performance or optimize resource usage in stream and graph processing. It can be used to satisfy performance requirements (e.g., throughput, lag) in stream processing by reducing the effort that applications need to process datasets. There are currently multiple stream processing platforms, and most of them do not natively support approximate results. A recent one, Stateful Functions, is an API that uses Flink to enable developers to easily build stream and graph processing applications. It also retains Flink's features like stateful computations, fault-tolerance, scalability, control events and its graph processing library Gelly. Herein we present Approxate, an extension over this platform to support approximate results. It can also support more efficient stream and graph processing by allocating available resources adaptively, driven by user-defined requirements on throughput, lag, and latency. This extension enables flexibility in computational trade-offs such as trading accuracy for performance. The user can choose which metrics should be guaranteed at the cost of others, and/or the accuracy. Approxate incorporates approximate computing (using load shedding) with adaptive accuracy and resource manegement in state-of-the-art stream processing platforms, which are not targeted in other relevant related work. It does not require significant modifications to application code, and minimizes imbalance in data source representation when dropping events.
具有近似计算和弹性缩放的有状态自适应流
近似计算模型可用于提高流和图形处理的性能或优化资源使用。它可以通过减少应用程序处理数据集所需的工作量来满足流处理中的性能要求(例如,吞吐量、延迟)。目前有多种流处理平台,其中大多数都不支持近似结果。最近的一个API是Stateful Functions,它使用Flink使开发人员能够轻松地构建流和图形处理应用程序。它还保留了Flink的特性,如有状态计算、容错、可扩展性、控制事件和图形处理库Gelly。在这里,我们提出了近似,在这个平台上的扩展,以支持近似结果。它还可以根据用户定义的吞吐量、延迟和延迟需求,自适应地分配可用资源,从而支持更高效的流和图形处理。这个扩展使计算权衡的灵活性,如交易精度的性能。用户可以选择以牺牲其他指标和/或准确性为代价来保证哪些指标。在最先进的流处理平台中,approximate结合了具有自适应精度和资源管理的近似计算(使用负载减少),这在其他相关工作中不是针对的。它不需要对应用程序代码进行重大修改,并且在删除事件时最大限度地减少数据源表示中的不平衡。
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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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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