A Review of AI and Multi-Agent Systems for Cloud Performance and Security

Cloud computing has become a critical backbone for distributed systems, offering scalability and flexibility across diverse industries. However, ensuring optimal performance and robust security in such dynamic environments presents significant challenges, including inefficient task scheduling, suboptimal resource utilization, and persistent security threats such as data breaches and Distributed Denial of Service (DDoS) attacks. This paper examines the transformative potential of Artificial Intelligence (AI) and Multi-Agent Systems (MAS) in addressing these complexities. AI-driven solutions, including real-time anomaly detection, predictive analytics, and resource optimization, are combined with MAS frameworks that leverage decentralized, autonomous agents for distributed decision-making and proactive threat mitigation. The integration of AI and MAS enables dynamic adaptation to workload fluctuations, enhances resource efficiency, and provides robust security measures in multi-cloud and large-scale systems. The paper further explores key challenges in implementing these technologies, such as scalability and integration across heterogeneous environments, and identifies promising research directions to advance their adoption. By synthesizing empirical evidence and recent advancements, this study highlights the critical role of AI and MAS in shaping the future of cloud performance and security.