面向边缘视频分析的内容感知联合旋钮配置和资源分配

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Tong Bai;Bo Hou;Zhipeng Wang;Dong Liu;Arumugam Nallanathan
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引用次数: 0

摘要

边缘计算具有易于低延迟响应的特点,能够支持实时视频分析应用,构成了边缘视频分析范式,其中联合旋钮配置和网络调度设计引起了越来越多的研究关注。然而,由于技术的限制,边缘视频分析的潜力尚未得到充分利用。i)视频内容对准确率表现的显著影响被忽略了。ii)调度中没有充分考虑可调变量。iii)基于启发式算法的解决方案远不是最优的。为了填补这一空白,本文提出了一种用于边缘视频分析的内容感知联合旋钮配置和资源分配方案。具体而言,利用从视频内容中提取的特征,提出了一种基于深度神经网络(DNN)的预测器来实时预测配置精度性能。在预测结果的帮助下,我们通过优化变量,包括分辨率、帧率、视频分析模型、网络带宽和受延迟约束的计算资源,将精度最大化问题表述为整数规划问题。为了有效地解决这一问题,我们设计了一种新颖的低复杂度动态规划方法。仿真结果验证了内容感知的关节旋钮配置和资源分配方案的有效性。在定量上,在目标检测场景中,依靠所提出的方案,在精度方面达到了3.3%的上界差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Content-Aware Joint Knob Configuration and Resource Allocation for Edge Video Analytics
Characterized by its ease of low-latency response, edge computing is capable of supporting real-time video analytics applications, constituting an edge video analytics paradigm, where the joint knob configuration and network scheduling design has drawn ever-escalating research attention. However, the potential of edge video analytics has not been fully exploited, owing to the limitations of the state-of-the-art as follows. i) The eminent impact of video content on accuracy performance has been ignored. ii) The variables that can be tuned are not fully considered in scheduling. iii) The heuristic algorithm-based solutions are far from the optimal. To fill in this gap, in this paper, we conceive a content-aware joint knob configuration and resource allocation scheme for edge video analytics. Concretely, fed with the features extracted from the video content, a deep neural network (DNN)-based predictor is proposed to predict the configuration-accuracy performance in a real-time manner. With an aid of the predictive results, we formulate an accuracy-maximization problem as an integer programming problem, by optimizing the variables, including resolution, frame rate, video analytic model, network bandwidth, and computational resource subject to the latency constraints. To solve this problem in an efficient manner, we devise a novel low-complexity dynamic programming method. Simulation results verify the efficiency of our content-aware joint knob configuration and resource allocation scheme. Quantitatively, a 3.3% gap is attained towards the upper bound in terms of the accuracy in an object detection scenario, relying on the scheme proposed.
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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