In this article, we address the resource allocation and monetization challenges in Mobile Edge Computing (MEC) systems, where users have heterogeneous demands and compete for high quality services. We formulate the Edge Resource Allocation Problem ( ${{\sf ERAP}}$ ) as a Mixed-Integer Linear Program ( ${{\sf MILP}}$ ) and prove that ${{\sf ERAP}}$ is ${{\sf NP}}$ -hard. To solve the problem efficiently, we propose two resource allocation mechanisms. First, we develop an auction-based mechanism and prove that the proposed mechanism is individually-rational and produces envy-free allocations . We also propose an ${{\sf LP}}$ -based approximation mechanism that does not guarantee envy-freeness, but it provides solutions that are guaranteed to be within a given distance from the optimal solution. We evaluate the performance of the proposed mechanisms by conducting an extensive experimental analysis on ${{\sf ERAP}}$ instances of various sizes. We use the optimal solutions obtained by solving the ${{\sf MILP}}$ model using a commercial solver as benchmarks to evaluate the quality of solutions. Our analysis shows that the proposed mechanisms obtain near optimal solutions for fairly large size instances of the problem in a reasonable amount of time.

中文翻译：

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移动边缘计算系统中的资源分配和定价机制


在本文中，我们解决了移动边缘计算（MEC）系统中的资源分配和货币化挑战，其中用户具有异构需求并争夺高质量服务。我们将边缘资源分配问题 ( ${{\sf ERAP}}$ ) 表述为混合整数线性规划 ( ${{\sf MILP}}$ ) 并证明 ${{\sf ERAP}}$ 是 $ {{\sf NP}}$ - 困难。为了有效地解决问题，我们提出了两种资源分配机制。首先，我们开发了一种基于拍卖的机制，并证明所提出的机制是个体理性的，并且产生无嫉妒的分配。我们还提出了一种基于 ${{\sf LP}}$ 的近似机制，它不保证无嫉妒，但它提供的解决方案保证与最优解决方案在给定距离内。我们通过对不同大小的 ${{\sf ERAP}}$ 实例进行广泛的实验分析来评估所提出机制的性能。我们使用商业求解器求解 ${{\sf MILP}}$ 模型获得的最优解作为基准来评估解的质量。我们的分析表明，所提出的机制在合理的时间内为问题的相当大的实例获得了接近最优的解决方案。