Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing
Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing
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DOI：
10.1109/tmm.2018.2865661
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发表时间：
2018-08
影响因子：
7.3
通讯作者：
Huy Trinh;P. Calyam;D. Chemodanov;Shizeng Yao;Qing Lei;Fan Gao;K. Palaniappan
中科院分区：
文献类型：
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作者：
Huy Trinh;P. Calyam;D. Chemodanov;Shizeng Yao;Qing Lei;Fan Gao;K. Palaniappan
移动边缘计算（MEC）等新范例正在变得可行，例如，在灾难事件响应期间进行实时决策，以处理网络边缘发生的数据洪流。然而，今天的MEC部署缺乏灵活的物联网设备数据处理，例如处理实时与节能处理的用户偏好。此外，MEC还可以受益于基于策略的边缘路由，以有效的能源消耗来处理持续的性能水平。在本文中，我们研究了MEC的潜力，以解决与有限电源的受限物联网设备上的能源管理相关的应用问题，同时还提供高分辨率下生成的视觉数据的低延迟处理。使用在灾难事件响应场景中很重要的面部识别应用程序，我们提出了一种新的“卸载决策”算法，该算法分析了在低到高工作负载下卸载视觉数据处理（即到边缘云或核心云）的计算策略中的权衡。该算法还分析了不同视觉数据消费需求下（即厚客户端用户和瘦客户端用户）对决策能耗的影响。为了解决处理吞吐量与能源效率之间的权衡，我们提出了一种“基于可持续策略的智能驱动边缘路由”算法，该算法在移动自组织网络中使用机器学习。该算法具有能源意识，并提高了地理路由基线性能（即最小化局部最小值的影响），以实现吞吐量性能的可持续性，同时还支持灵活的策略规范。我们通过在GENI云基础设施中的现实边缘和核心云测试平台上进行实验来评估我们提出的算法，并在模拟中重建龙卷风破坏的灾难场景。我们的实证结果表明，MEC可以为希望通过低延迟节能的用户提供灵活性，反之亦然，在面部识别应用程序的视觉数据处理中。此外，我们的仿真结果表明，我们的路由方法在不同用户偏好、节点移动性和严重节点故障条件下都优于现有的解决方案。
New paradigms such as Mobile Edge Computing (MEC) are becoming feasible for use in, e.g., real-time decision-making during disaster incident response to handle the data deluge occurring in the network edge. However, MEC deployments today lack flexible IoT device data handling such as handling user preferences for real-time versus energy-efficient processing. Moreover, MEC can also benefit from a policy-based edge routing to handle sustained performance levels with efficient energy consumption. In this paper, we study the potential of MEC to address application issues related to energy management on constrained IoT devices with limited power sources, while also providing low-latency processing of visual data being generated at high resolutions. Using a facial recognition application that is important in disaster incident response scenarios, we propose a novel “offload decision-making” algorithm that analyzes the tradeoffs in computing policies to offload visual data processing (i.e., to an edge cloud or a core cloud) at low-to-high workloads. This algorithm also analyzes the impact on energy consumption in the decision-making under different visual data consumption requirements (i.e., users with thick clients or thin clients). To address the processing-throughput versus energy-efficiency tradeoffs, we propose a “Sustainable Policy-based Intelligence-Driven Edge Routing” algorithm that uses machine learning within Mobile Ad hoc Networks. This algorithm is energy aware and improves the geographic routing baseline performance (i.e., minimizes impact of local minima) for throughput performance sustainability, while also enabling flexible policy specification. We evaluate our proposed algorithms by conducting experiments on a realistic edge and core cloud testbed in the GENI Cloud infrastructure, and recreate disaster scenes of tornado damages within simulations. Our empirical results show how MEC can provide flexibility to users who desire energy conservation over low latency or vice versa in the visual data processing with a facial recognition application. In addition, our simulation results show that our routing approach outperforms existing solutions under diverse user preferences, node mobility, and severe node failure conditions.