  The rapid growth of time-sensitive applications and services has driven
enhancements to computing infrastructures. The main challenge that needs
addressing for these applications is the optimal placement of the end-users
demands to reduce the total power consumption and delay. One of the widely
adopted paradigms to address such a challenge is fog computing. Placing fog
units close to end-users at the edge of the network can help mitigate some of
the latency and energy efficiency issues. Compared to the traditional
hyperscale cloud data centres, fog computing units are constrained by
computational power, hence, the capacity of fog units plays a critical role in
meeting the stringent demands of the end-users due to intensive processing
workloads. In this paper, we aim to optimize the placement of virtual machines
(VMs) demands originating from end-users in a fog computing setting by
formulating a Mixed Integer Linear Programming (MILP) model to minimize the
total power consumption through the use of a federated architecture made up of
multiple distributed fog cells. The obtained results show an increase in
processing capacity in the fog layer and a reduction in the power consumption
by up to 26% compared to the Non-Federated fogs network.
