Approximate Edge Analytics for the IoT Ecosystem

TL;DR

This paper proposes APPROXIOT, an approximate computing system for IoT data analytics that uses hierarchical stratified reservoir sampling on edge devices to enable faster insights with controlled error bounds.

Contribution

It introduces a novel hierarchical stratified reservoir sampling algorithm and implements APPROXIOT for real-time IoT analytics with proven efficiency improvements.

Findings

Achieves 1.3X to 9.9X speedup over simple random sampling.

Provides approximate results with rigorous error bounds.

Effective in real-world IoT case studies.

Abstract

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 trade-off 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 1.3X-9.9X with varying sampling fraction of 80% to 10% compared to simple random sampling.