  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.
