Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method

Fog computing is the new technology era, which is deployed as a middle layer computing system between Internet of Things (IoT) devices and cloud computing systems, where data are acquired and analyzed at the border of the system. Cloud computing offers many advantages, and drawbacks of network congestions due to the huge amount of information coming from various sources, which causes higher latency for immediate responsive devices. To conquer these problems fog computing provides solutions as they are deployed near the edge of end users. The load examination concern arises in fog computing when a great amount of new IoT user applications are connected to the fog nodes. To efficiently handle load balancing, a particle swarm optimization-based Enhanced Dynamic Resource Allocation Method (EDRAM) has been proposed which in turn reduces task waiting time, latency and network bandwidth consumption and improves the Quality of Experience (QoE). The Enhanced Dynamic Resource Allocation Method (EDRAM), which in turns helps for allocating the required resource by removing the long-time inactive, unreferenced and sleepy services from the Random-Access Memory.