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In the evolving landscape of distributed systems and microservices, API gateways play a pivotal role in managing, monitoring, and securing service-to-service communication. Traditional API management approaches often lack adaptability to dynamic workloads, varying user contexts, and evolving threat landscapes. This paper introduces a novel approach to API management by leveraging Reinforcement Learning (RL) to create a smart, context-aware API gateway that adapts its policies in real-time based on environmental cues and usage patterns. The system incorporates a context detection module to interpret variables such as request frequency, source behavior, authentication status, and network load. A Reinforcement Learning agent dynamically adjusts API rate limits, caching strategies, and access policies to optimize performance, security, and reliability. Through simulation and comparative analysis, the proposed model demonstrates improved response time, reduced latency, and enhanced resilience against misuse and attacks. This work aims to bridge the gap between static rule-based API management and intelligent adaptive systems by enabling self-learning gateways tailored for modern cloud-native applications.
Keywords:
Context-Aware API Management Using Reinforcement Learning
Cite Article:
"Context-Aware API Management Using Reinforcement Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 6, page no.681-690, June-2024, Available :http://www.ijrti.org/papers/IJRTI2406100.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator