Published Paper Details:

The rapid expansion of cloud-based applications and microservices has intensified the demand for robust API management strategies. AI-THROTTLE introduces an intelligent throttling mechanism that harnesses predictive machine learning models to dynamically regulate API traffic and optimize resource allocation. By analyzing historical and real-time data, the system forecasts usage patterns and detects anomalies before potential overloads occur, thereby enabling proactive adjustments in rate limiting. The framework employs a combination of regression analysis and classification algorithms to fine-tune throttling parameters, ensuring that service quality is maintained even during peak demand. Experimental evaluations conducted on simulated high-traffic environments demonstrate significant reductions in latency and improved throughput compared to conventional static throttling methods. In addition, case studies on enterprise-level deployments reveal that AI-THROTTLE effectively mitigates system downtime and enhances overall reliability. The adaptive learning component continuously refines predictive accuracy by integrating feedback from ongoing operations, allowing the system to evolve in response to changing usage trends. Challenges related to computational overhead and data privacy are addressed through efficient algorithm design and robust security protocols. Overall, AI-THROTTLE represents a significant advancement in API management, offering a scalable and resilient solution for modern distributed systems. This research underscores the transformative potential of integrating artificial intelligence with traditional network management techniques to achieve smarter, more responsive API services. Extensive simulations and real-world deployments further validate the efficiency of AI-THROTTLE, demonstrating its ability to seamlessly integrate with existing infrastructures while reducing system bottlenecks and ensuring continuous, high-quality service delivery. These results highlight the promise of adaptive API control.