Dynamic Workload Balancing Strategies for IoT Based Fog Network

In the Internet of Things (IoT) landscape, fog computing has established itself as a method in response to the increasing requirements associated with data-intensive and latency-sensitive programs. In the case of fog networks, data is processed closer to the edge using spread resources and this helps to avoid bandwidth consumption and latency. Due to heterogeneous features of IoT environments, workload balancing is left to run its course, which has declined overall performance and less efficiently using resources. In this study, new loadbalancing strategies for various fog networks based on the Internet of Things. These strategies are meant to increase system scalability and reliability, reduces bottlenecks, and optimizes resource allocation. The strategies makes use of machine learning and real-time data analytics to ensure a more flexible manner for distributing computing jobs among fog nodes. It also takes into account the needs of the application, device capabilities, and network conditions. These techniques' associated rules for dynamic relocation, prediction of operational burden on topological models, and intelligent decision-making processes are essential parts. Together, they can predict fluctuations in load, continually evaluate and monitor network parameters, and lay out resources effectively to keep high-performing at all points in time. The core theme of this article will be to explore the integration of edge intelligence and fog orchestration techniques. This, in turn, will help to optimize resources and make task allocation more autonomous.