Artificial Intelligence (AI) has recently seen widespread adoption across a range of business sectors and industries leading to the development of numerous ground-breaking applications and services that are mainly supported by edge computing high-performance processing and storage capabilities.Everything has been transformed into disruptive innovation and digitalized as a result of Industry 4.0. Along with many opportunities, cloud federation also presents resource allocation issues.The range and processing speed of IoT-based devices in industries will be significantly increased with the cooperation of AI at the edge. The multi-objective optimization of the AI Driven Mechanism algorithm is described in this investigation for the deployment of dynamic applications in edge computing federations. This should be adjusted according to the needs of each specific application. This analysis suggests a data reliability architecture for edge artificial intelligence-based IoT devices as well as an approach for the Edge Federation Architecture (BASMATI).The outcomes of several iterations of simulation show that the evolutionary algorithm, which is efficient,may offer the best options for VM (Virtual Machine) placementdecisions, energy efficiency, and can be adjusted to meet particular application requirements.

Artificial Intelligence (AI), Edge Computing, Edge federation, resource allocation, Genetic Algorithm (GA)