Optimized Fog Colony Framework for Efficient Service Placement using Hybrid Genetic Algorithm

The transition from Cloud Computing to Fog Computing is being investigated as the preferred method for rapid data processing in Internet of Things (IoT) applications. Fog computing is a decentralized computing paradigm, harnesses the computational capabilities of IoT devices at the network edge to perform data processing and provide real-time services. However, effective service placement in fog environments remains a challenge. This research paper focuses on the lack of efficient techniques for organizing fog devices into optimal fog colonies that possess self-rule capabilities and require minimal energy for administration. We propose a hierarchical clustering-based fog colony architecture to fill this void, which will allow for in-depth investigation and analysis of various colony configurations. The resulting dendrogram includes all feasible colony configurations, and a genetic algorithm is used for precise colony selection based on fitness and accuracy. The multi-objective hybrid genetic algorithm NSGA-II is used to assess how efficiently the suggested framework performs under a variety of conditions. The goal is to reduce the delay between an app's initial interaction with a user and the initial practice of the app. The results show improvements in reaching predefined goals when the proposed strategy is applied to different contexts.