  Fog computing is a promising computing paradigm for time-sensitive Internet
of Things (IoT) applications. It helps to process data close to the users, in
order to deliver faster processing outcomes than the Cloud; it also helps to
reduce network traffic. The computation environment in the Fog computing is
highly dynamic and most of the Fog devices are battery powered hence the
chances of application failure is high which leads to delaying the application
outcome. On the other hand, if we rerun the application in other devices after
the failure it will not comply with time-sensitiveness. To solve this problem,
we need to run applications in an energy-efficient manner which is a
challenging task due to the dynamic nature of Fog computing environment. It is
required to schedule application in such a way that the application should not
fail due to the unavailability of energy. In this paper, we propose a multiple
linear, regression-based resource allocation mechanism to run applications in
an energy-aware manner in the Fog computing environment to minimise failures
due to energy constraint. Prior works lack of energy-aware application
execution considering dynamism of Fog environment. Hence, we propose A multiple
linear regression-based approach which can achieve such objectives. We present
a sustainable energy-aware framework and algorithm which execute applications
in Fog environment in an energy-aware manner. The trade-off between
energy-efficient allocation and application execution time has been
investigated and shown to have a minimum negative impact on the system for
energy-aware allocation. We compared our proposed method with existing
approaches. Our proposed approach minimises the delay and processing by 20%,
and 17% compared with the existing one. Furthermore, SLA violation decrease by
57% for the proposed energy-aware allocation.
