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<p>The rapid deployment of 5G networks has introduced significant challenges in managing scarce and valuable resources, such as core network cache memory. Ensuring efficient and fair allocation of these resources across heterogeneous service slices is essential for maintaining high Quality of Experience (QoE) and guaranteeing system-wide stability. This paper presents a novel service-aware cache allocation framework for 5G network slicing that leverages cooperative game theory to achieve fairness and efficiency. The proposed approach operates in three phases: (i) an admission control and prioritization mechanism that dynamically ranks slices based on critical Quality of Service (QoS) metrics—latency, throughput, reliability, and availability; (ii) an initial proportional allocation that respects guaranteed minimum resources while aligning with priority scores; and (iii) a cooperative bankruptcy game model, where the Shapley value is applied to redistribute cache resources under demand overload conditions equitably. Extensive simulations demonstrate that the <span>Proposed Method</span> outperforms conventional proportional fairness (PF) and max-min fairness (MMF) schemes, achieving up to 3% higher Jain’s fairness index, 3% improvement in QoE fairness, and approximately 4% higher slice satisfaction. These results highlight the framework’s effectiveness in delivering fair, stable, and service-oriented cache allocation in 5G network slicing environments.</p>

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Fairness-aware cache resource allocation in 5G network slicing via cooperative bankruptcy game theory

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月18日

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  • Mostafa Bamdad,
  • Shahram Jamali,
  • Reza Fotohi

摘要

The rapid deployment of 5G networks has introduced significant challenges in managing scarce and valuable resources, such as core network cache memory. Ensuring efficient and fair allocation of these resources across heterogeneous service slices is essential for maintaining high Quality of Experience (QoE) and guaranteeing system-wide stability. This paper presents a novel service-aware cache allocation framework for 5G network slicing that leverages cooperative game theory to achieve fairness and efficiency. The proposed approach operates in three phases: (i) an admission control and prioritization mechanism that dynamically ranks slices based on critical Quality of Service (QoS) metrics—latency, throughput, reliability, and availability; (ii) an initial proportional allocation that respects guaranteed minimum resources while aligning with priority scores; and (iii) a cooperative bankruptcy game model, where the Shapley value is applied to redistribute cache resources under demand overload conditions equitably. Extensive simulations demonstrate that the Proposed Method outperforms conventional proportional fairness (PF) and max-min fairness (MMF) schemes, achieving up to 3% higher Jain’s fairness index, 3% improvement in QoE fairness, and approximately 4% higher slice satisfaction. These results highlight the framework’s effectiveness in delivering fair, stable, and service-oriented cache allocation in 5G network slicing environments.