Developing ReAdaBalancer for Load Balancing Optimization in Networked Cloud Computing
M. Diarmansyah Batubara, Poltak Sihombing, Syahril Efendi, Suherman Suherman · International Journal of Advanced Computer Science and Applications · 2025
Traditional load balancing systems frequently have trouble adjusting to abrupt and unexpected changes in traffic. This can cause problems like server overload, longer response times, and more requests being denied. This problem is highly important in areas like healthcare, finance, cloud computing, and e-commerce, where performance, stability, and fast data delivery are all very important. To solve this problem, this study presents ReAdaBalancer, an adaptive load balancing architecture that aims to improve system performance, scalability, stability, and efficiency in contexts with changing traffic. Flask serves as the backend framework for ReAdaBalancer, while Nginx serves as the load balancer. Real-time monitoring and analytics are used to improve traffic distribution based on the resources that are currently available. Leveraging queuing theory (M/M/s/K Network), the system’s performance is tested under diverse load situations, providing insights into its scalability and efficiency. ReAdaBalancer can also learn and adapt all the time, thanks to machine learning and heuristic optimization. This makes sure that it works the same way even when demand changes. Experimental results demonstrate that, under equivalent settings, ReAdaBalancer decreases response times by over 67% and reduces request denial rates by over 50% in comparison to traditional methods. This work has multiple opportunities for subsequent investigation. Future improvements could involve making ReAdaBalancer work in distributed multi-data center environments, adding reinforcement learning to make decisions more independently, looking into load balancing strategies that use less energy, and making it work in edge computing and IoT ecosystems.