Predictive API Performance Optimization using Machine Learning for Large-Scale Enterprise Platforms

Arnout Schuijff; Arnout Schuijff

doi:10.15662/IJSRAT.2023.0606009

Predictive API Performance Optimization using Machine Learning for Large-Scale Enterprise Platforms

Arnout Schuijff

CTO and Architect, Adyen, Netherlands

Abstract

Application Programming Interfaces (APIs) have become the foundation of modern enterprise software ecosystems by enabling seamless communication among distributed applications, cloud services, microservices, and external business partners. As enterprise platforms continue to expand in scale and complexity, maintaining consistent API performance has become increasingly challenging due to fluctuating workloads, network congestion, resource limitations, and evolving user demands. Traditional API performance management techniques primarily rely on reactive monitoring and static optimization strategies, which often fail to anticipate performance degradation before it affects service quality. This research proposes a predictive API performance optimization framework that leverages machine learning techniques to forecast performance bottlenecks, optimize resource allocation, and improve service reliability in large-scale enterprise platforms. The proposed framework integrates real-time monitoring, predictive analytics, anomaly detection, workload forecasting, and intelligent decision-making to proactively optimize API operations. Machine learning models continuously analyze historical and live operational data to identify patterns associated with latency, throughput, error rates, and resource utilization. Predictive insights enable automated scaling, intelligent request routing, and proactive fault mitigation before service disruptions occur. The research adopts a conceptual methodology supported by an extensive review of existing literature and architectural analysis. The proposed framework demonstrates significant potential to enhance API responsiveness, reduce operational costs, improve system scalability, strengthen service availability, and support the efficient operation of modern enterprise platforms operating in dynamic and highly distributed computing environments

Article Information

Journal	International Journal of Science, Research and Technology
Volume (Issue)	Vol. 6 No. 6 (2023): International Journal of Science, Research and Technology (IJSRAT)
DOI	https://doi.org/10.15662/IJSRAT.2023.0606009
Pages	11093-11104
Published	November 19, 2023
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Arnout Schuijff (%2023). Predictive API Performance Optimization using Machine Learning for Large-Scale Enterprise Platforms. International Journal of Science, Research and Technology , Vol. 6 No. 6 (2023): International Journal of Science, Research and Technology (IJSRAT) , pp. 11093-11104. https://doi.org/10.15662/IJSRAT.2023.0606009

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