# Energy Efficient Load-Balancing Mechanism in Integrated IoT-Fog-Cloud Environment
## 3 Methodology  
With the tremendous growth in the field of cloud technologies, load balancing and efficient energy consumption have become very difficult tasks. Many cloud providers are available in the market, which provide various services such as pay-as-you-go with a flexible infrastructure. Two key issues arise in the IoT-fog-cloud model: firstly, the declining performance of fog gateways with continuously increasing fog devices; and secondly, the increased congestion in fog devices and the fog gateway transmission link data that are overly delayed. These issues are the repercussions of the dynamic behavior of the IoT-fog-cloud environment. Therefore, it is important to ponder the dynamic nature of the IoT-fog-cloud structure and develop an efficient algorithm to cater to the abovementioned issues. The following principles are followed while designing and implementing the algorithm for the dynamic IoT-fog-cloud environment:  
* Fog nodes are prioritized overcloud nodes. When data flows are imbalanced at fog devices or gateways, data will be forwarded to the cloud to utilize cloud nodes. * Cloud nodes will be utilized only when fog nodes are congested or drained of power. * Computational delay and latency, load balancing, and energy usage are computed in the IoT-fog-cloud network. * A few fog nodes or gateways can be deactivated when the computing demand is low to conserve energy. System Model  
Cisco introduced the idea of fog computing in 2014, and it provides the distributed infrastructure for IoT to expand capabilities and computing power to the network's edge [1]. In the fog model, data is managed and controlled by the end users, such as local clouds. As an example, as represented in Figure 2, there are fog nodes, N IoT nodes, and a central cloud node. Each IoT node generates lots of service requests. The traffic models are as follows: at the IoT node level, M/M/1 [28] queue; at the fog node, M/M/c [29; 30] queue; at the cloud node, M/M/\(\infty\). Each IoT device forwards the request to the fog node using a \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Methodology Adopted** & **Features/Parameters** & **Limitation** \\ \hline
[16] & Proposed the utilization of queuing models to investigate the transmission delays, energy usage, and cost incurred for offloading tasks in the cloud–fog scenarios & Power consumption, latency, and cost in the restricted environment \\ \hline
[17] & Devised a strategy for a heterogeneous network to improvise green energy utilization and flow level throughput & Throughput and power consumption & Limited to the extent of architecture \\ \hline
[18] & Focused on efficient load balancing for fog environment and devised the Dynamic Energy-Efficient Resource Allocation strategy & Computational cost, load balancing, and energy consumption & Lack of fault tolerance \\ \hline
[21] & An efficient framework is presented that considers the trade-off of power usage and delay in the integrated cloud–fog computing structure & Power consumption and delay & Optimization is not achieved in a distributed environment \\ \hline
[23] & A dynamic energy-efficient load balancing mechanism is proposed to deal with IoT resource allocation dynamically & Energy utilization, load balancing, and cost of computing & Service migration and security concerns \\ \hline
[25] & A potential mechanism for cascading failure in edge-assisted IoT is proposed & Real-time data packets and link congestion & Varied network configurations with different topologies are not considered \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._wireless medium. If the maximal acceptance rate of the fog node is higher than the request rate, then the fog node will process all the requests. Otherwise, the fog device will send the traffic to the cloud centrally for execution. 1.