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@article{201046,
        author = {Mrs.T.Praveena and Jansi thara. R and Kamali.A and Nisha.P and Kousalya. G},
        title = {AI-Driven Predictive Load Balancing Framework for Distributed Cloud Servers},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {246-252},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201046},
        abstract = {Cloud computing environments support large-scale applications with dynamic and unpredictable workloads. Traditional load balancing techniques rely on static or reactive algorithms that fail to anticipate sudden traffic spikes, leading to server overload, increased response time, and inefficient resource utilization.
This project proposes an AI-driven predictive load balancing framework implemented entirely using software tools. The system collects real-time and historical server metrics and applies machine learning models to predict future workload conditions. Based on these predictions, the framework intelligently distributes incoming requests to the most suitable servers. The proposed system improves scalability, reliability, performance efficiency, and resource optimization without requiring additional hardware.},
        keywords = {Cloud Computing, Load Balancing, Machine Learning, Distributed Systems, Predictive Analytics, Kubernetes, Docker, AI-based Scheduling.},
        month = {May},
        }