Bridging the Edge-Cloud Barrier for Real-time Advanced Vision Analytics


Yiding Wang, Weiyan Wang, and Junxue Zhang, HKUST; Junchen Jiang, University of Chicago; Kai Chen, HKUST


Advanced vision analytics plays a key role in a plethora of real-world applications. Unfortunately, many of these applications fail to leverage the abundant compute resource in cloud services, because they require high computing resources {\em and} high-quality video input, but the (wireless) network connections between visual sensors (cameras) and the cloud/edge servers do not always provide sufficient and stable bandwidth to stream high-fidelity video data in real time.

This paper presents CloudSeg, an edge-to-cloud framework for advanced vision analytics that co-designs the cloud-side inference with real-time video streaming, to achieve both low latency and high inference accuracy. The core idea is to send the video stream in low resolution, but recover the high-resolution frames from the low-resolution stream via a {\em super-resolution} procedure tailored for the actual analytics tasks. In essence, CloudSeg trades additional cloud-side computation (super-resolution) for significantly reduced network bandwidth. Our initial evaluation shows that compared to previous work, CloudSeg can reduce bandwidth consumption by $\sim$6.8$\times$ with negligible drop in accuracy.

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@inproceedings {234849,
author = {Yiding Wang and Weiyan Wang and Junxue Zhang and Junchen Jiang and Kai Chen},
title = {Bridging the {Edge-Cloud} Barrier for Real-time Advanced Vision Analytics},
booktitle = {11th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 19)},
year = {2019},
address = {Renton, WA},
url = {},
publisher = {USENIX Association},
month = jul,