Deep Q-Learning-Based Adaptive API Integration Framework for Dynamic Workflow Reconfiguration in Microservices Architectures

Raja M Hussain

Authors

Raja M Hussain Solutions Engineer Author

Keywords:

Deep Q-Learning, microservices, dynamic workflows, API integration, reinforcement learning, adaptive systems, service orchestration

Abstract

Microservices architectures, while offering scalability and flexibility, face challenges in dynamically reconfiguring workflows in response to fluctuating runtime conditions such as service latency, API failure, or workload surges. Traditional rule-based and static integration mechanisms are ill-suited for real-time adaptive decision-making. This study introduces a novel Deep Q-Learning-based adaptive API integration framework designed to autonomously reconfigure workflows in microservices systems. By leveraging reinforcement learning, particularly deep Q-networks (DQNs), the system learns optimal integration strategies under varying performance metrics and context shifts. The framework is validated using a simulated e-commerce environment where dynamic service selection and workflow reconfiguration are critical. Experimental results demonstrate that our approach significantly improves response time, fault tolerance, and system throughput compared to static and heuristic-based strategies.

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