Review of Artificial Intelligence Based Cloud Cost and Security Optimization Techniques Systems

Abstract

Cloud computing has gained immense importance as  a critical technological infrastructure for present scalable,  flexible, and cost-efficient computing services to businesses  across the globe. But achieving efficient operations cost-wise  along with providing high-level security continues to pose major  challenges owing to the growing complexity of cloud  environments. Artificial Intelligence (AI) has proven to be a  potent tool that helps with intelligent resource management,  prediction, automated scaling, and enhanced security. The  current paper proposals an extensive review of AI-powered  cloud cost and security optimization strategies. The paper  reviews the main AI technologies, such as Machine Learning  (ML), Deep Learning (DL), and Reinforcement Learning, and  analyzes their application in resource prediction, intelligent  workload scheduling, auto-scaling, energy-efficient resource  management, threat detection, intrusion detection, anomaly  detection, and access control. It has been found that AI-based  strategies result in better utilization of resources, cost efficiency, scalability, and enhanced security of cloud systems against  emerging cybersecurity threats. The paper highlights the  limitations of AI technologies and the existing challenges,  including lack of explain ability, data privacy, adversarial  attacks, high computational cost, and scalability issues. The  overall study has shown the capabilities that can be achieved by  using artificial intelligence to develop intelligent, secure, and  efficient cloud computing systems.