AI-Driven Autonomous API Orchestration for Multi-Cloud Integration

Abstract

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The effective processing of API calls by cloud providers such as AWS, Microsoft Azure, or GCP has become increasingly challenging as multi-cloud adoption has increased. Reliability, cost maintenance, latency reduction, and performance improvement are some advantages of using several clouds. Traditional approaches frequently fail because they don't respond well to the dynamic and shifting nature of workloads in multi-cloud environment. We solve this by introducing a deep Q-network-based AI approach for autonomous API request routing. This provides reinforcement learning for picking the most relevant and valuable cloud provider based on live metric parameters such as latency, cost, and failure rates. The system remains versatile, subject to changing conditions, to ensure that performance optimization is achieved across AWS, Azure, and GCP. These findings show significant improvements and accomplishments in key performance indicators (KPI). API latency has been reduced from 250 ms to 140 ms, approximately a 44% reduction, with a corresponding decrease in the cost per request of 20%. The success rate has increased from 92% to 98%, and the failure rate has decreased from 8% to 2%. There is also a balanced load across the three clouds <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(34 \%, 33 \%$</tex>, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$33 \%)$</tex> with an energy savings of 10%. Thus, our results demonstrate that AI-enhanced reinforcement learning can be highly beneficial in enhancing multi-cloud API orchestration. Besides these, they will also be resource-efficient and therefore very scalable solutions for the modern-day cloud architectures.