Adaptively Compressing IoT Data on the Resource-constrained Edge


Tao Lu, Marvell Technology Group; Wen Xia and Xiangyu Zou, Harbin Institute of Technology, Shenzhen, China; Qianbin Xia, Marvell Technology Group


Big IoT data needs to be frequently moved between edge and cloud for efficient analysis and storage. Data movement is costly in low-bandwidth wide area network environments. Data compression can dramatically reduce data size to mitigate the bandwidth bottleneck. However, compression is compute-intensive and compression throughput can be limited by available CPU resources. The impact of available computation capability of the resource-constrained edge on the edge-to-cloud data transfer rate is apparent. Our study reveals compressors, including gzip, bzip2, lzma, and zstd, perform very differently under various resource-constrained conditions. This motivates us to propose models for the best compressor selection under CPU, network, and storage resource limitation conditions on the edge. We implement ZipMate, a middleware that enables resource-aware and adaptive compression policy based on the model. Our evaluation shows that adaptive policies consistently outperform unitary or random compressor selection policies.

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@inproceedings {253368,
author = {Tao Lu and Wen Xia and Xiangyu Zou and Qianbin Xia},
title = {Adaptively Compressing IoT Data on the Resource-constrained Edge},
booktitle = {3rd {USENIX} Workshop on Hot Topics in Edge Computing (HotEdge 20)},
year = {2020},
url = {},
publisher = {{USENIX} Association},
month = jun,

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