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Optimizing Kubernetes with Multi-Objective Scheduling Algorithms: A 5G Perspective
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DOI：10.3390/computers14090390.png)
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Abstract
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This review provides an in-depth examination of multi-objective scheduling algorithms within 5G networks, with a particular focus on Kubernetes-based container orchestration. As 5G systems evolve, efficient resource allocation and the optimization of Quality-of-Service (QoS) metrics, including response time, energy efficiency, scalability, and resource utilization, have become increasingly critical. Given the scheduler’s central role in orchestrating containerized workloads, this study analyzes diverse scheduling strategies designed to address these competing objectives. A novel taxonomy is introduced to categorize existing approaches, offering a structured view of deterministic, heuristic, and learning-based methods. Furthermore, the review identifies key research challenges, highlights open issues, such as QoS-aware orchestration and resilience in distributed environments, and outlines prospective directions to advance multi-objective scheduling in Kubernetes for next-generation networks. By synthesizing current knowledge and mapping research gaps, this work aims to provide both a foundation for newcomers and a practical reference for advancing scholarly and industrial efforts in the field.
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