The evolution of the Internet of Things (IoT) has been driving the explosive growth of deep neural network (DNN)-based applications and processing demands. Hence, edge computing has emerged as a potential solution to meet these processing requirements. However, emerging IoT applications have increasingly demanded to run multiple DNNs to extract multifaceted knowledge, requiring more computational resources and increasing response time. Consequently, edge nodes cannot act as a complete substitute for the previous cloud paradigm, owing to their relatively limited resources. To address this problem, we propose to incorporate nearby IoT devices when allocating resources to multiple DNN models. Furthermore, the optimization of resource allocation can be hindered by the heterogeneity of IoT devices, which affects the delay performance of DNN-based computing. In this context, we propose a DNN partition placement and resource allocation strategy that considers different processing powers, memory, and battery levels for heterogeneous IoT devices. We evaluate the performance of the proposed strategy through extensive simulations. Simulation results reveal that the proposed strategy outperforms other existing solutions in terms of end-to-end delay, service probability, and energy consumption. The proposed solution was further simulated in a Kubernetes testbed consisting of actual devices to assess its feasibility.

中文翻译：

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异构物联网环境中多个基于 DNN 的应用程序的分区放置和资源分配

物联网 (IoT) 的发展一直在推动基于深度神经网络 (DNN) 的应用程序和处理需求的爆炸式增长。因此，边缘计算已成为满足这些处理要求的潜在解决方案。然而，新兴的物联网应用程序越来越需要运行多个 DNN 来提取多方面的知识，这需要更多的计算资源并增加响应时间。因此，由于资源相对有限，边缘节点不能完全替代以前的云范式。为了解决这个问题，我们建议在将资源分配给多个 DNN 模型时合并附近的物联网设备。此外，物联网设备的异构性可能会阻碍资源分配的优化，这会影响基于 DNN 的计算的延迟性能。在此背景下，我们提出了一种 DNN 分区放置和资源分配策略，该策略考虑了异构物联网设备的不同处理能力、内存和电池水平。我们通过广泛的模拟评估所提出策略的性能。仿真结果表明，所提出的策略在端到端延迟、服务概率和能量消耗方面优于其他现有解决方案。所提出的解决方案在由实际设备组成的 Kubernetes 测试台中进一步模拟，以评估其可行性。我们通过广泛的模拟评估所提出策略的性能。仿真结果表明，所提出的策略在端到端延迟、服务概率和能量消耗方面优于其他现有解决方案。所提出的解决方案在由实际设备组成的 Kubernetes 测试台中进一步模拟，以评估其可行性。我们通过广泛的模拟评估所提出策略的性能。仿真结果表明，所提出的策略在端到端延迟、服务概率和能量消耗方面优于其他现有解决方案。所提出的解决方案在由实际设备组成的 Kubernetes 测试台中进一步模拟，以评估其可行性。