A Load Balancing Algorithm for Cloud-Fog-based IoT Networks using Bald Eagle Search Optimization

The exponential growth of Internet of Things (IoT) usage causes the network congestion, delay-response while using cloud computing. Fog computing, a closer cloud service provider near edge network is popular for providing service to latency sensitive real-time data. The united cloud-fog model can be benefited to handle both real-time and latency-delayed services that minimizes cost and energy consumption. The large-scale real-time services to distributed and limited resource-based fog node lead a load balancing problem where nodes are unevenly loaded with underload or overload. Here a Bald Eagle Search based load balance algorithm is proposed that can evenly balance the load as well as optimally allocate resources of fog node. The performance results are evaluated and validated which reduces the energy consumption and increases makespan and percentage of resource utilization.