An Analysis of Cloud Cost Optimization Using Machine Learning

Cloud cost always remains a major concern for organizations and individuals. To reduce a major chunk of cost, various machine learning algorithms could be adopted for predictive resource scalability. That would also help in the elimination of underutilization and overutilization of resources. It would also facilitate workload prediction to minimize energy consumption, reduce costs, effectively allocate resources, and provide high-quality services. It also strives for cost optimization by resource management that could be achieved through containerization, multi-resource management, and job scheduling. Over the years, machine learning (ML) has evolved as a powerful tool for automation approaches, providing autoscaling features. A new approach to cloud resource management optimization and prediction capability employs a hybrid solution which combines LSTM neural networks with reinforcement learning methods. Estimating resource requirements by analyzing historical data and automating scaling decisions. This paper provides a gist of how machine learning approaches could overcome the cost challenges by optimizing resource allocation based on real-time needs and reducing unused capacity.