The growing demand for edge computing resources, particularly due to increasing popularity of Internet of Things (IoT), and distributed machine/deep learning applications poses a significant challenge. On the one hand, certain edge service providers (ESPs) may not have sufficient resources to satisfy their applications according to the associated service-level agreements. On the other hand, some ESPs may have additional unused resources. In this paper, we propose a resource-sharing framework that allows different ESPs to optimally utilize their resources and improve the satisfaction level of applications subject to constraints such as communication cost for sharing resources across ESPs. Our framework considers that different ESPs have their own objectives for utilizing their resources, thus resulting in a multi-objective optimization problem. We present an $N$-person \emph{Nash Bargaining Solution} (NBS) for resource allocation and sharing among ESPs with \emph{Pareto} optimality guarantee. Furthermore, we propose a \emph{distributed}, primal-dual algorithm to obtain the NBS by proving that the strong-duality property holds for the resultant resource sharing optimization problem. Using synthetic and real-world data traces, we show numerically that the proposed NBS based framework not only enhances the ability to satisfy applications' resource demands, but also improves utilities of different ESPs.

中文翻译：

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边缘资源共享：分布式讨价还价理论方法

对边缘计算资源不断增长的需求，特别是由于物联网 (IoT) 和分布式机器/深度学习应用的日益普及，带来了重大挑战。一方面，根据相关的服务级别协议，某些边缘服务提供商 (ESP) 可能没有足够的资源来满足其应用程序。另一方面，一些 ESP 可能有额外的未使用资源。在本文中，我们提出了一个资源共享框架，该框架允许不同的 ESP 优化利用其资源，并提高受限制的应用程序的满意度，例如跨 ESP 共享资源的通信成本。我们的框架认为不同的 ESP 有自己的资源利用目标，从而导致多目标优化问题。我们提出了一个 $N$-person \emph{Nash Bargaining Solution} (NBS)，用于具有 \emph{Pareto} 最优保证的 ESP 之间的资源分配和共享。此外，我们提出了一种 \emph{distributed} 原始对偶算法，通过证明强对偶属性适用于结果资源共享优化问题来获得 NBS。使用合成和真实世界的数据轨迹，我们从数值上表明，所提出的基于 NBS 的框架不仅增强了满足应用程序资源需求的能力，而且还提高了不同 ESP 的效用。primal-dual 算法通过证明强对偶性质适用于结果资源共享优化问题来获得 NBS。使用合成和真实世界的数据轨迹，我们从数值上表明，所提出的基于 NBS 的框架不仅增强了满足应用程序资源需求的能力，而且还提高了不同 ESP 的效用。primal-dual 算法通过证明强对偶性质适用于结果资源共享优化问题来获得 NBS。使用合成和真实世界的数据轨迹，我们从数值上表明，所提出的基于 NBS 的框架不仅增强了满足应用程序资源需求的能力，而且还提高了不同 ESP 的效用。