标题： 图神经网络和强化学习用于主动应用图像放置 显示英文标题

标题： Graph Neural Networks and Reinforcement Learning for Proactive Application Image Placement

摘要： 从云计算向云边连续体的转变，为数据密集型和交互式应用带来了新的机遇和挑战。 近年来，边缘计算引起了工业界和学术界的广泛关注，成为满足下一代应用日益严格需求的关键使能技术。 在边缘计算中，计算被放置得更接近终端用户，以促进低延迟和高带宽的应用和服务。 然而，边缘计算的分布式、动态和异构特性，给服务部署带来了重大挑战。 边缘计算的一个关键方面是管理应用程序在网络系统中的部署，以最小化每个应用程序的运行时间，同时考虑系统设备上的可用资源和系统的网络能力。 应用程序镜像的部署必须进行主动规划，以最小化镜像传输时间，并满足应用程序的严格需求。 在这方面，本文提出了一种结合图神经网络和基于策略梯度的强化学习的主动镜像部署方法，并通过实验评估并与各种解决方案进行了比较。 研究结果表明，尽管所提出的方案在某些情况下可能导致较长的执行时间，但在应用程序部署方面始终能够获得更优的结果。 显示英文摘要

摘要： The shift from Cloud Computing to a Cloud-Edge continuum presents new opportunities and challenges for data-intensive and interactive applications. Edge computing has garnered a lot of attention from both industry and academia in recent years, emerging as a key enabler for meeting the increasingly strict demands of Next Generation applications. In Edge computing the computations are placed closer to the end-users, to facilitate low-latency and high-bandwidth applications and services. However, the distributed, dynamic, and heterogeneous nature of Edge computing, presents a significant challenge for service placement. A critical aspect of Edge computing involves managing the placement of applications within the network system to minimize each application's runtime, considering the resources available on system devices and the capabilities of the system's network. The placement of application images must be proactively planned to minimize image tranfer time, and meet the strict demands of the applications. In this regard, this paper proposes an approach for proactive image placement that combines Graph Neural Networks and actor-critic Reinforcement Learning, which is evaluated empirically and compared against various solutions. The findings indicate that although the proposed approach may result in longer execution times in certain scenarios, it consistently achieves superior outcomes in terms of application placement.