ApproxIoT: Approximate Analytics for Edge Computing
ApproxIoT: Approximate Analytics for Edge Computing
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DOI：
10.1109/icdcs.2018.00048
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Z. Wen;D. Quoc;Pramod Bhatotia;Ruichuan Chen;Myungjin Lee
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文献类型：
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作者：
Z. Wen;D. Quoc;Pramod Bhatotia;Ruichuan Chen;Myungjin Lee
支持IoT的设备继续生成大量数据。将这些不断到达的原始数据转化为及时的见解对于许多现代在线服务至关重要。对于这样的设置，对整个数据集进行数据分析的传统形式对于支持实时流分析来说将是非常有限和昂贵的。在这项工作中，我们为物联网环境中的数据分析提供了近似计算的案例。近似计算的目的是高效地执行工作流，其中近似输出就足够了，而不是精确输出。近似计算背后的想法是在一个代表性的样本而不是整个输入数据集上进行计算。因此，近似计算-基于所选择的样本大小-可以在输出精度和计算效率之间进行系统的权衡。这激发了APPROXIOT的设计-物联网近似计算的数据分析系统。为了实现这一想法，我们设计了一个在线分层分层水库采样算法，使用边缘计算资源，以产生具有严格误差界的近似输出。为了展示我们算法的有效性，我们基于Apache Kafka实现了APPROXIOT，并使用一组微基准测试和真实案例研究来评估其有效性。我们的结果表明，与简单随机采样相比，在80%到10%的采样率下，APPROXIOT实现了1：3×-9：9 ×的加速比。
IoT-enabled devices continue to generate a massive amount of data. Transforming this continuously arriving raw data into timely insights is critical for many modern online services. For such settings, the traditional form of data analytics over the entire dataset would be prohibitively limiting and expensive for supporting real-time stream analytics. In this work, we make a case for approximate computing for data analytics in IoT settings. Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing– based on the chosen sample size – can make a systematic tradeoff between the output accuracy and computation efficiency. This motivated the design of APPROXIOT– a data analytics system for approximate computing in IoT. To realize this idea, we designed an online hierarchical stratified reservoir sampling algorithm that uses edge computing resources to produce approximate output with rigorous error bounds. To showcase the effectiveness of our algorithm, we implemented APPROXIOT based on Apache Kafka and evaluated its effectiveness using a set of microbenchmarks and real-world case studies. Our results show that APPROXIOT achieves a speedup 1:3×–9:9× with varying sampling fraction of 80% to 10% compared to simple random sampling.