A Novel Edge-Cloud Interworking Framework in the Video Analytics of the Internet of Things
A Novel Edge-Cloud Interworking Framework in the Video Analytics of the Internet of Things
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DOI：
10.1109/lcomm.2019.2943857
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
Sanghong Ahn;Joohyung Lee;Tae Yeon Kim;J. Choi
中科院分区：
文献类型：
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作者：
Sanghong Ahn;Joohyung Lee;Tae Yeon Kim;J. Choi
这封信提出了物联网(IoT)视频分析中的一种新颖的边缘-云互通框架，该框架包括针对计算密集型视频分析应用的经济高效的作业负载均衡和调度方案。该框架旨在最大限度地降低云资源使用成本，同时保证在执行并发操作时的最后期限。给出了一个两阶段混合整数问题的公式及其启发式贪婪算法，该算法捕获了所有相互交织的目标。数值分析表明，该框架在货币成本和服务时延方面均优于已有的方案，并具有一定的实际复杂度。
This letter proposes a novel edge-cloud interworking framework in the video analytics of the Internet of Things (IoT) that consists of cost-effective job load balancing and scheduling schemes for computation-intensive video analytics applications. The proposed framework aims to minimize the cost of cloud resource usage while guaranteeing deadlines when conducting concurrent operations. A formulation of a two-stage mixed-integer problem and its heuristic greedy algorithms is presented, which captures all intertwined goals. From the numerical analysis, we reveal that the proposed framework outperforms the existing schemes in terms of monetary cost and service latency with a practical complexity bound.