Machine Learning-Driven Performance Optimization Framework for Highly Scalable Kubernetes and Cloud-Native Systems
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706034Keywords:
Machine Learning, Kubernetes, Cloud-Native Computing, Performance Optimization, Autoscaling, Resource Management, Intelligent Scheduling, Reinforcement Learning, Container Orchestration, Cloud Computing, Scalability, Workload Prediction, Resource Allocation, MicroservicesAbstract
The rapid adoption of Kubernetes and cloud-native architectures has transformed the way modern enterprises deploy, manage, and scale distributed applications. However, the dynamic and heterogeneous nature of cloud-native environments creates significant challenges in resource allocation, workload scheduling, autoscaling, latency management, energy efficiency, and cost optimization. Conventional performance-management approaches typically depend on static thresholds or manually configured policies, which may be inadequate for workloads characterized by unpredictable demand and complex resource interactions. This study proposes a Machine Learning-Driven Performance Optimization Framework designed to enhance the scalability, efficiency, reliability, and cost-effectiveness of Kubernetes-based cloud-native systems. The proposed framework integrates continuous telemetry collection, workload forecasting, anomaly detection, resource-demand prediction, intelligent autoscaling, and adaptive scheduling into a unified optimization architecture. Machine learning models analyze historical and real-time metrics, including CPU utilization, memory consumption, network traffic, request latency, throughput, pod behavior, and workload patterns, to predict future resource requirements and dynamically adjust infrastructure configurations. The framework further incorporates reinforcement learning and predictive optimization mechanisms to enable adaptive decision-making under changing workload conditions. Performance is evaluated using scalability, response time, resource utilization, throughput, service-level objective compliance, energy consumption, and infrastructure cost. By combining machine learning with Kubernetes-native orchestration capabilities, the proposed approach seeks to move cloud-native performance management from reactive resource control toward proactive, autonomous, and intelligent optimization
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