• Giridhar MV
• Chahithi Santhosh Shetty
• Nikhil Kanthi
• Dr. Jayanthi PN

Cloud computing has revolutionized the way com- putational resources are managed and deployed. However, system failures remain a critical issue affecting performance and reliabil- ity. Traditional fault tolerance methods prioritize recovery rather than preemptive fault prediction. This paper explores AI-driven fault prediction techniques that enhance resource optimization in cloud computing environments. We discuss machine learning models used for fault detection and provide insights into their effectiveness through empirical analysis. The results highlight the superiority of proactive fault detection in minimizing downtime and improving resource efficiency.Cloud computing uses a network of virtualized computers to provide on-demand access to computing resources like storage and processing power. With the help of this technology, businesses may deploy and adapt services according to their requirements, guaranteeing efficiency and flexibility. Software as a Service (SaaS), which offers applications on a subscription basis; Platform as a Service (PaaS), which offers development tools for creating applications; and Infrastructure as a Service (IaaS), which provides access to necessary computing infrastructure like servers and storage, are the three main models that make up cloud environments. Prominent suppliers like Microsoft Azure and Amazon Web Services (AWS) use these concepts to offer scaled computing solutions. As central computing units, virtual machines (VMs) function in cloud settings. Effective virtual machine management is essential for performance since it facilitates fault tolerance and smooth migration. By enabling dynamic resource allocation, virtualization maximizes system utilization. Cloud migration is moving virtual machines (VMs) between servers in accordance with workload requirements and efficiency measures. Cloud computing improves total service reliability, reduces interrup- tions, and maximizes energy consumption by guaranteeing efficient resource allocation.Logical and physical resources must be balanced as part of cloud computing resource man- agement. High availability is maintained by avoiding overuse and system crashes through efficient task management. Fault tolerance and load balancing are two tactics that guarantee operational effectiveness. Cloud settings can improve speed and fault tolerance by utilizing distributed services, which will decrease downtime and increase scalability.

Cloud Computing, Fault Prediction, Machine Learning, Resource Optimization, Virtual Machines.

"Artificial Intelligence-Based Fault Prediction for Cloud Resource Efficiency", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.g543-g546, February-2025, Available :http://www.jetir.org/papers/JETIR2502662.pdf

"Artificial Intelligence-Based Fault Prediction for Cloud Resource Efficiency", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppg543-g546, February-2025, Available at : http://www.jetir.org/papers/JETIR2502662.pdf