Policy-Based Function-Centric Computation Offloading for Real-Time Drone Video Analytics
Policy-Based Function-Centric Computation Offloading for Real-Time Drone Video Analytics
复制标题
用于实时无人机视频分析的基于策略、以功能为中心的计算卸载
DOI：
10.1109/lanman.2019.8847112
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Calyam, Prasad
中科院分区：
文献类型：
--
作者：
Chemodanov, Dmitrii;Qu, Chengyi;Opeoluwa, Osunkoya;Wang, Songjie;Calyam, Prasad
计算机视觉应用程序越来越多地用于无人机等移动物联网设备。它们在灾难/事件响应或人群抗议管理场景中提供实时支持，例如，通过清点人员/车辆或识别人脸/物体。然而，由于设备上的计算能力有限，在地理分布区域部署用于实时视频分析的此类应用程序在处理密集型富媒体数据以满足用户的体验质量(QOE)期望方面提出了新的挑战。在本文中，我们提出了一种新的基于策略的决策计算卸载方案，该方案不仅有助于在性能与成本之间进行权衡，而且有助于将决策卸载到用于实时视频分析的边缘、云或功能中心计算资源体系结构。为了评估我们的卸载方案，我们将现有的用于对象/运动检测和对象分类的计算机视觉流水线分解为一系列基于容器的微服务函数，这些函数通过REST风格的API进行通信。我们在一个真实的地理分布的边缘/核心云测试床上，使用不同的策略和计算体系结构来评估我们的方案的性能。结果显示，在实时无人机视频分析期间，我们的方案如何利用最先进的计算卸载技术在性能(即每秒帧数)与成本因素(使用Amazon Web Services Lambda定价)之间进行帕累托最优权衡，从而培养有效的环境态势感知。
Computer vision applications are increasingly used on mobile Internet-of-Things (IoT) devices such as drones. They provide real-time support in disaster/incident response or crowd protest management scenarios by e.g., counting human/vehicles, or recognizing faces/objects. However, deployment of such applications for real-time video analytics at geo-distributed areas presents new challenges in processing intensive media-rich data to meet users' Quality of Experience (QoE) expectations, due to limited computing power on the devices. In this paper, we present a novel policy-based decision computation offloading scheme that not only facilitates trade-offs in performance vs. cost, but also aids in offloading decision to either an Edge, Cloud or Function-Centric Computing resource architecture for real-time video analytics. To evaluate our offloading scheme, we decompose an existing computer vision pipeline for object/motion detection and object classification into a chain of container-based micro-service functions that communicate via a RESTful API. We evaluate the performance of our scheme on a realistic geo-distributed edge/core cloud testbed using different policies and computing architectures. Results show how our scheme utilizes state-of-the-art computation offloading techniques to Pareto-optimally trade-off performance (i.e., frames-per-second) vs. cost factors (using Amazon Web Services Lambda pricing) during real-time drone video analytics, and thus fosters effective environmental situational awareness.
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影响因子：
7.3
作者：
Huy Trinh;P. Calyam;D. Chemodanov;Shizeng Yao;Qing Lei;Fan Gao;K. Palaniappan
通讯作者：
Huy Trinh;P. Calyam;D. Chemodanov;Shizeng Yao;Qing Lei;Fan Gao;K. Palaniappan
DOI：
10.1109/infocom.2019.8737498
发表时间：
2019-04
期刊：
IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
影响因子：
--
作者：
D. Chemodanov;P. Calyam;Flavio Esposito
通讯作者：
D. Chemodanov;P. Calyam;Flavio Esposito
DOI：
10.3390/s18113751
发表时间：
2018-11-02
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Valentino R;Jung WS;Ko YB
通讯作者：
Ko YB
影响因子：
3.5
作者：
Grasso, Christian;Schembra, Giovanni
通讯作者：
Schembra, Giovanni