  Recently, to deliver services directly to the network edge, fog computing, an
emerging and developing technology, acts as a layer between the cloud and the
IoT worlds. The cloud or fog computing nodes could be selected by IoTs
applications to meet their resource needs. Due to the scarce resources of fog
devices that are available, as well as the need to meet user demands for low
latency and quick reaction times, resource allocation in the fog-cloud
environment becomes a difficult problem. In this problem, the load balancing
between several fog devices is the most important element in achieving resource
efficiency and preventing overload on fog devices. In this paper, a new
adaptive resource allocation technique for load balancing in a fog-cloud
environment is proposed. The proposed technique ranks each fog device using
hybrid multi-criteria decision-making approaches Fuzzy Analytic Hierarchy
Process (FAHP) and Fuzzy Technique for Order Performance by Similarity to Ideal
Solution (FTOPSIS), then selects the most effective fog device based on the
resulting ranking set. The simulation results show that the proposed technique
outperforms existing techniques in terms of load balancing, response time,
resource utilization, and energy consumption. The proposed technique decreases
the number of fog nodes by 11%, load balancing variance by 69% and increases
resource utilization to 90% which is comparatively higher than the comparable
methods.
