OsmoticGate: Adaptive Edge-Based Real-Time Video Analytics for the Internet of Things
OsmoticGate: Adaptive Edge-Based Real-Time Video Analytics for the Internet of Things
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DOI：
10.1109/tc.2022.3193630
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发表时间：
2023-04
影响因子：
3.7
通讯作者：
Bin Qian;Z. Wen;Junqi Tang;Ye Yuan-;Albert Y. Zomaya;R. Ranjan
中科院分区：
文献类型：
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作者：
Bin Qian;Z. Wen;Junqi Tang;Ye Yuan-;Albert Y. Zomaya;R. Ranjan
边缘计算近年来发展势头迅猛，可以对流视频数据进行更即时的分析。然而，边缘设备通常缺乏计算能力（处理能力、存储器）来保证合理的性能（例如，准确性、延迟、吞吐量）来执行复杂的视频分析任务。为了缓解这一关键问题，普遍的趋势是将一些视频分析任务从边缘设备卸载到云端。然而，现有的卸载方法未能考虑视频分析任务的动态性质（例如，针对不同视频内容改变编码格式）并且不能适应系统动态（例如，在边缘和云之间变化的工作负载）。为了克服现有方法的局限性，我们开发了一个基于分层队列概念的边缘云卸载性能模型。资源限制（例如，计算能力和网络带宽）和动态边缘云网络条件来参数化性能模型。由于找到性能模型的最佳解决方案是NP难的，我们开发了一个两阶段的基于梯度的算法，并将其与一些最先进的（SOTA）解决方案（例如，FastVA、DeepDecision、Hill Climbing）。实验表明，我们的性能模型的优势和稳定性的建议卸载方法给定不同的系统（边缘云）和视频分析应用程序的动态。
Edge computing has gained momentum in recent years, and can provide more immediate analysis of streaming video data. However, the edge devices often lack the computing capabilities (processing power, memory) to guarantee reasonable performance (e.g., accuracy, latency, throughput) for complex video analytics tasks. To alleviate this critical problem, the prevalent trend is to offload some video analytics tasks from the edge devices to the cloud. However, existing offloading approaches fail to consider the dynamic nature of the video analytical tasks (e.g., varying encoding format for different video content) and are unable to adapt system dynamics (e.g., varying workload between the edge and the cloud). To overcome the limitation of existing approaches, we develop an edge-cloud offloading performance model based on the concept of hierarchical queues. The resource constraints (e.g., computing capacity and network bandwidth) of each edge nodes and dynamic edge-cloud network conditions are used to parameterize the performance model. Since finding optimal solutions for the performance model is NP-hard, we develop a two-stage gradient-based algorithm and compare it with some state-of-the-art (SOTA) solutions (e.g., FastVA, DeepDecision, Hill Climbing). Experiments have shown our performance model's advantages and the stability of the proposed offloading approach given different systems (edge-cloud) and video analytics application dynamics.