Fog computing based cost optimization for university governance

This study presented a new architecture based on fog computing to effectively reduce the burdensome cost of university governance. The process is established by enhancing network performance and optimizing resource utilization. The solution uses an effective process to overcome the inherent shortcomings of traditional cloud-based systems, which incur exorbitant costs and have delayed response times, especially in the case of distributed computing arrangements in online education. The main contribution of this work is a low cost, fog based model that uniquely combines a new cost function for optimizing resources allocation with a fuzzy inference system for intelligent error handling and resource prioritization. Our approach can assist significantly in alleviating the computational burden in the evolving paradigm of online education and distributed computing in the on-premise or hybrid cloud environment. Simulation results conducted in MATLAB environment, validate cost minimization and resource optimization in the university networks using the proposed solution. Although, this study is an important addition to the existing knowledge base on the use of fog computing in university governance, and it lays the groundwork for future research into optimizing operations of administrative processes and lowering costs of educational institutions.