Deep Neural Networks (DNNs) have served as a catalyst in introducing a plethora of next-generation services in the era of Internet of Things (IoT), thanks to the availability of massive amounts of data collected by the objects on the edge. Currently, DNN models are used to deliver many Artificial Intelligence (AI) services that include image and natural language processing, speech recognition, and robotics. Accordingly, such services utilize various DNN models that make it computationally intensive for deployment on the edge devices alone. Thus, most AI models are offloaded to distant cloud data centers (CDCs), which tend to consolidate large amounts of computing and storage resources into one or more CDCs. Deploying services in the CDC will inevitably lead to excessive latencies and overall increase in power consumption. Instead, fog computing allows for cloud services to be extended to the edge of the network, which allows for data processing to be performed closer to the end-user device. However, different from cloud data centers, fog nodes have limited computational power and are highly distributed in the network. In this paper, using Mixed Integer Linear Programming (MILP), we formulate the placement of DNN inference models, which is abstracted as a network embedding problem in a Cloud Fog Network (CFN) architecture, where power savings are introduced through trade-offs between processing and networking. We study the performance of the CFN architecture by comparing the energy savings when compared to the baseline approach which is the CDC.
翻译:深心内网(DNN)是推动在物联网时代引入大量下一代服务的催化剂,这是因为在边缘物体收集了大量数据,目前,DNN模型被用于提供许多人工智能(AI)服务,包括图像和自然语言处理、语音识别和机器人等。因此,这类服务利用了多种DNNN模型,使DNN模型在计算上集中,仅用于边缘装置。因此,大多数AI模型被卸载到遥远的云数据中心(CDCs),这些中心往往将大量计算和存储资源合并成一个或多个CDCs。在CDC中部署服务将不可避免地导致过度延迟和电力消耗的总体增加。相反,雾计算使云服务扩展到网络边缘,从而使得数据处理能够更接近终端用户装置。然而,与云数据中心不同,雾节点方法的计算能力有限,并且高度分布在网络中。在本文中,使用混合的Intear 线性规划(MILP) 将网络的网络化模型比对网络的网络进行网络化。我们通过CNUF的网络的网络结构来将云化。