  The Internet of Things (IoT) is gaining momentum in its quest to bridge the
gap between the physical and the digital world. The main goal of the IoT is the
creation of smart environments and self-aware things that help to facilitate a
variety of services such as smart transport, climate monitoring, e-health, etc.
Huge volumes of data are expected to be collected by the connected
sensors/things, which in traditional cases are processed centrally by large
data centers in the core network that will inevitably lead to excessive
transportation power consumption as well as added latency overheads. Instead,
fog computing has been proposed by researchers from industry and academia to
extend the capability of the cloud right to the point where the data is
collected at the sensing layer. This way, primitive tasks that can be hosted in
IoT sensors do not need to be sent all the way to the cloud for processing. In
this paper we propose energy efficient embedding of machine learning (ML)
models over a cloud-fog network using a Mixed Integer Linear Programming (MILP)
optimization model. We exploit virtualization in our framework to provide
service abstraction of Deep Neural Networks (DNN) layers that can be composed
into a set of VMs interconnected by virtual links. We constrain the number of
VMs that can be processed at the IoT layer and study the impact on the
performance of the cloud fog approach.
