INTELLIGENT SERVICE ORCHESTRATION IN MICROSERVICES USING MACHINE LEARNING FOR DYNAMIC RESOURCE OPTIMIZATION
Keywords:
Microservices, Machine Learning, Resource Optimization, Service Orchestration, Edge Computing, Dynamic Scaling, Containerization, Reinforcement LearningSynopsis
Purpose: This study investigates the integration of machine learning (ML) in orchestrating microservices to enhance resource allocation dynamically and intelligently in distributed computing environments.
Design/methodology/approach: A comprehensive approach combining reinforcement learning, predictive analytics, and orchestration tools like Kubernetes is used. The system monitors service load, anticipates spikes, and automates scaling using ML-based decisions.
Findings: ML-based orchestration improves resource efficiency by up to 30%, reduces latency, and enhances system scalability and adaptability under fluctuating workloads.
Practical implications: These findings support the deployment of ML-enhanced orchestrators in edge-cloud systems, enabling automated, cost-efficient service management for IoT, real-time analytics, and smart city platforms.
Originality/value: Unlike static orchestration, this work emphasizes adaptive, learning-based strategies, pioneering self-optimizing microservice environments.
References
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