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@article{176048,
        author = {Likhith U C and Dhananjay S J and Rajath Singh R and Rudramurthy V C},
        title = {Predictive Network Load Balancer For Real-Time Traffic Management On AWS.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4653-4659},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176048},
        abstract = {In today’s digital landscape, cloud-based applications face growing challenges in managing unpredictable and dynamic network traffic. Traditional load balancers operate reactively and often fall short during unexpected surges, leading to performance degradation, underutilization of resources, and increased costs. This paper presents a predictive network load balancing system with real-time traffic management on AWS, designed to proactively optimize infrastructure based on machine learning traffic forecasts. The system leverages AWS services such as EC2, Auto Scaling, and CloudWatch, alongside a custom machine learning model to analyze historical and real-time data for predicting traffic spikes. Upon anticipating high demand, resources are automatically scaled, and traffic is intelligently routed using load balancing algorithms. This integrated approach enhances availability, minimizes latency, and improves resource utilization while reducing operational costs. Experimental validation confirms that predictive scaling outperforms reactive models by ensuring smoother performance during peak loads and maintaining efficient usage during low-traffic periods. This solution is scalable, cost-effective, and adaptable across diverse applications like e-commerce, media streaming, and enterprise software.},
        keywords = {Predictive Load Balancer, AWS Auto Scaling, Machine Learning, EC2, Real-Time Traffic Management, Cloud Optimization.},
        month = {April},
        }