OPTIMIZING CLOUD COSTS AND CARBON FOOTPRINT IN MULTI-CLOUD ENVIRONMENTS WITH FUZZY LOGIC & MONTE CARLO SIMULATION

Organizations are using multi-cloud architectures increasingly to address performance and scalability needs in a world where data is king. But higher operational expenses and carbon emissions are regular results, which is bad for the environment. Using fuzzy logic and Monte Carlo simulations, this study presents a novel hybrid optimization method for lowering the cost and carbon footprint of cloud computing. The suggested solution changes the way cloud resources are allocated in real time based on things like workload, pricing, and the weather. Fuzzy logic may help you make decisions when the inputs are not clear, and Monte Carlo simulations can help you figure out how much effort and how much money you need. When we compare our technique to static baseline approaches, testing using real-world datasets demonstrate that our approach cuts operational expenses by 25% and carbon emissions by 33%. These findings suggest that there is space for clever, flexible ways to manage resources in a manner that is good for the environment across several clouds.