INTELLIGENT RESOURCE MANAGEMENT IN CONTAINERIZED ORCHESTRATION SYSTEMS
Author(s): Kalesha Khan Pattan
Publication #: 2510017
Date of Publication: 10.01.2023
Country: United States
Pages: 1-22
Published In: Volume 9 Issue 1 January-2023
DOI: https://doi.org/10.5281/zenodo.17475893
Abstract
The increasing complexity of modern containerized infrastructures demands intelligent mechanisms for efficient resource management, scalability, and performance optimization. Traditional static resource allocation models often result in either resource underutilization or performance bottlenecks due to their inability to adapt to fluctuating workloads. This research focuses on dynamic resource scaling in containerized orchestration systems, a technique that enhances performance and efficiency by continuously monitoring workload variations and automatically adjusting computing resources such as CPU and memory in real time. The proposed model introduces a dynamic scaling mechanism that relies on continuous system metrics collection, real-time performance evaluation, and adaptive scaling decisions based on workload intensity and cluster conditions. The experimental setup involves containerized orchestration environments operating across multiple cluster sizes (3, 5, 7, 9, and 11 nodes). Key performance parameters such as CPU utilization, memory utilization, and response time are analyzed before and after implementing the dynamic scaling mechanism. Results demonstrate a significant improvement across all performance indicators: These findings confirm that dynamic scaling effectively minimizes idle resource time, balances workloads efficiently, and ensures higher throughput without manual intervention. The evaluation also reveals that as cluster size increases, the benefits of dynamic scaling become more prominent due to enhanced coordination and distributed workload balancing. This leads to improved energy efficiency, faster processing, and reduced latency in service delivery. The proposed framework can be integrated with existing orchestration tools to achieve real-time elasticity and improved resource utilization for large-scale deployments. In conclusion, dynamic resource scaling represents a significant step toward building self-optimizing container orchestration systems. By enabling proactive resource adjustment, it not only enhances system responsiveness and stability but also lays the foundation for sustainable, high-performance cloud and edge infrastructures. This study establishes a strong foundation for future extensions involving predictive scaling, cost-aware optimization, and energy-efficient scheduling models to further enhance distributed containerized environments.
Keywords: Scaling, Containers, Orchestration, Workload, Resource, Performance, Efficiency, Utilization, Clusters, Automation, Elasticity, Optimization, Resilience.
Download/View Count: 149
Share this Article