针对多接入边缘计算中高效 D2D 辅助计算卸载和资源分配的近端策略优化

Future Internet Pub Date : 2024-01-02 DOI:10.3390/fi16010019
Chen Zhang, Celimuge Wu, Min Lin, Yangfei Lin, William Liu
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引用次数: 0

摘要

在先进的 5G 及更先进的网络中,多接入边缘计算(MEC)越来越被认为是一项前景广阔的技术,它具有双重优势:既能降低云数据中心的能源利用率,又能满足终端设备对可靠性和实时响应能力的要求。然而,MEC 网络固有的复杂性和多变性给计算卸载决策带来了巨大挑战。为了解决这个问题,我们提出了一种基于近端策略优化(PPO)的设备到设备(D2D)辅助计算卸载和资源分配方案。我们构建了一个现实的 MEC 网络环境,并开发了一个马尔可夫决策过程 (MDP) 模型,该模型能最大限度地减少时间损失和能源消耗。通过整合基于 D2D 通信的卸载框架,可以在终端设备和 MEC 服务器之间协同卸载任务,从而提高资源利用率和计算效率。利用深度强化学习中的 PPO 算法对 MDP 模型进行求解,从而得出卸载和资源分配的最优策略。与三种基准方法的广泛比较分析证实了我们的方案在延迟、能耗和算法收敛性方面的优越性能,证明了它在新兴的 5G 及其他技术背景下改善 MEC 网络运营的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proximal Policy Optimization for Efficient D2D-Assisted Computation Offloading and Resource Allocation in Multi-Access Edge Computing
In the advanced 5G and beyond networks, multi-access edge computing (MEC) is increasingly recognized as a promising technology, offering the dual advantages of reducing energy utilization in cloud data centers while catering to the demands for reliability and real-time responsiveness in end devices. However, the inherent complexity and variability of MEC networks pose significant challenges in computational offloading decisions. To tackle this problem, we propose a proximal policy optimization (PPO)-based Device-to-Device (D2D)-assisted computation offloading and resource allocation scheme. We construct a realistic MEC network environment and develop a Markov decision process (MDP) model that minimizes time loss and energy consumption. The integration of a D2D communication-based offloading framework allows for collaborative task offloading between end devices and MEC servers, enhancing both resource utilization and computational efficiency. The MDP model is solved using the PPO algorithm in deep reinforcement learning to derive an optimal policy for offloading and resource allocation. Extensive comparative analysis with three benchmarked approaches has confirmed our scheme’s superior performance in latency, energy consumption, and algorithmic convergence, demonstrating its potential to improve MEC network operations in the context of emerging 5G and beyond technologies.
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