With the widespread adoption of edge computing and the rollout of 5G technology, the edge network is experiencing rapid growth. Edge computing enables the execution of certain computational tasks on edge devices, fostering more efficient resource utilization. However, the reliability of the edge network is constrained by its network connections. Network instability can significantly compromise service quality. An effective service function chain (SFC) migration algorithm is essential to optimize resource utilization, enhance service quality. This paper begins by analyzing the current research landscape of edge networks and SFC migration algorithms. Subsequently, the challenges associated with edge network and SFC migration are formally articulated, leading to the proposal of a SFC migration algorithm based on deep reinforcement learning (DRL) with a focus on reliability assurance (RA-SFCM). The algorithm leverages multi-agent deep reinforcement learning to dynamically perceive changes in the edge network environment. It introduces an advantage function to evaluate the performance of each agent relative to the average level and incorporates a central attention mechanism with multiple attention heads to better capture the interdependencies and relationships among different agents. Additionally, this paper innovatively defines and quantifies the reliability of the migration process. By introducing a reliability penalty mechanism based on the migration target nodes and link capacity, it enhances the reliability of the migration schemes. The experimental results conclusively demonstrate the remarkable advantages of the RA-SFCM algorithm in terms of real-time performance, resource utilization efficiency, and reliability. Compared to algorithms such as Sa-VNFM, ROVM, and DLTSAC, RA-SFCM exhibits superior performance. For RA-SFCM, the optimized deployment migration strategy enhances real-time performance, precise resource management improves utilization efficiency, and advanced fault tolerance mechanisms strengthen reliability.

中文翻译：

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使用深度强化学习的边缘网络中保证可靠性的服务功能链迁移策略


随着边缘计算的广泛采用和5G技术的推出，边缘网络正在经历快速增长。边缘计算可以在边缘设备上执行某些计算任务，从而提高资源利用效率。然而，边缘网络的可靠性受到其网络连接的限制。网络不稳定会严重影响服务质量。有效的服务功能链（SFC）迁移算法对于优化资源利用率、提高服务质量至关重要。本文首先分析了边缘网络和SFC迁移算法的当前研究现状。随后，正式阐述了与边缘网络和 SFC 迁移相关的挑战，并提出了基于深度强化学习（DRL）并重点关注可靠性保证的 SFC 迁移算法（RA-SFCM）。该算法利用多智能体深度强化学习来动态感知边缘网络环境的变化。它引入了一个优势函数来评估每个智能体相对于平均水平的表现，并结合了具有多个注意力头的中央注意力机制，以更好地捕获不同智能体之间的相互依赖性和关系。此外，本文创新性地定义和量化了迁移过程的可靠性。通过引入基于迁移目标节点和链路容量的可靠性惩罚机制，增强了迁移方案的可靠性。实验结果证明了RA-SFCM算法在实时性、资源利用效率和可靠性方面的显着优势。 与Sa-VNFM、ROVM和DLTSAC等算法相比，RA-SFCM表现出优越的性能。对于RA-SFCM来说，优化的部署迁移策略增强了实时性，精确的资源管理提高了利用效率，先进的容错机制增强了可靠性。