Abstract
The growth of the Internet of Things (IoT) causes a significant amount of data to come in from physical devices and sensors, which adds to the latency and processing delays in smart grid applications. The pay-per-model method of transmitting gathered data that cloud computing offers improves scalability and functionality for end devices, which increases smart grid efficiency. Milliseconds matter in the crucial realms of load balancing, resource usage, and distribution systems, where any latency or jitter is unacceptable. By strategically positioning processing, networking, storage, and communication capabilities at the network edge, fog computing, an outgrowth of cloud technology, successfully addresses current issues in service groups. Three different load balancing algorithms are proposed in this research, which presents a novel hybrid model on a highly virtualized platform: throttled, round-robin, and a novel equilibrium optimizer with simulated annealing (EO-SA). In order to optimize services inside smart grids, the paper thoroughly analyzes and compares these load balancing algorithms, highlighting their significance for cost minimization and effective resource distribution.