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<p>In Mobile Edge Computing (MEC), efficient resource allocation and optimal Service Function Chain (SFC) assembly are critical challenges in Network Function Virtualization (NFV). Parallelizing network functions can mitigate the high latency caused by the increasing number of Virtual Network Functions (VNFs). However, existing approaches often assume static resource demands during SFC assembly, which may not reflect real-world conditions. To address this limitation, this paper presents a Deep Reinforcement Learning (DRL)-based framework that dynamically optimizes network function parallelism while considering resource demand fluctuations. The proposed strategy models the problem as a fairness-aware throughput maximization task, ensuring balanced resource allocation and improved service efficiency. By leveraging a learning-based strategy, our strategy adapts to dynamic network conditions, reducing service deployment latencies and enhancing overall performance. Extensive simulations demonstrate that our strategy outperforms the best state-of-the-art method in terms of average latency and deployment rate, achieving improvements of 4.3% and 1.98%, respectively.</p>

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Fairness-aware throughput maximization based on network function parallelism in MEC using deep reinforcement learning

  • 期刊论文

  • 发表日期：

    2025年8月13日

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  • Yanli Wang

摘要

In Mobile Edge Computing (MEC), efficient resource allocation and optimal Service Function Chain (SFC) assembly are critical challenges in Network Function Virtualization (NFV). Parallelizing network functions can mitigate the high latency caused by the increasing number of Virtual Network Functions (VNFs). However, existing approaches often assume static resource demands during SFC assembly, which may not reflect real-world conditions. To address this limitation, this paper presents a Deep Reinforcement Learning (DRL)-based framework that dynamically optimizes network function parallelism while considering resource demand fluctuations. The proposed strategy models the problem as a fairness-aware throughput maximization task, ensuring balanced resource allocation and improved service efficiency. By leveraging a learning-based strategy, our strategy adapts to dynamic network conditions, reducing service deployment latencies and enhancing overall performance. Extensive simulations demonstrate that our strategy outperforms the best state-of-the-art method in terms of average latency and deployment rate, achieving improvements of 4.3% and 1.98%, respectively.