This paper considers a scenario in which an access point (AP) is equipped with a server of finite computing power, and serves multiple resource-hungry users by charging users a price. This price helps to regulate users’ behavior in offloading jobs to the AP. However, existing works on pricing are based on abstract concave utility functions, giving no dependence on physical layer parameters. To that end, we first introduce a novel utility function, which measures the cost reduction by offloading as compared with executing jobs locally. Based on this utility function we then formulate two offloading games, with one maximizing individuals interest and the other maximizing the overall systems interest. We analyze the structural property of the games and admit in closed-form the Nash Equilibrium and the Social Equilibrium for the homogeneous user case, respectively. The proposed expressions are functions of user parameters such as the weights of time and energy, the distance from the AP, thus constituting an advancement over prior economic works that have considered only abstract functions. Finally, we propose an optimal price-based scheme, with which we prove that the interactive decision-making process with self-interested users converges to a Nash Equilibrium point equal to the Social Equilibrium point.

中文翻译：

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一种用于多访问边缘计算的激励感知作业卸载控制框架

本文考虑了一个场景，其中一个接入点（AP）配备了一个有限计算能力的服务器，通过向用户收费的方式为多个资源饥渴的用户提供服务。此价格有助于规范用户将作业卸载到 AP 的行为。然而，现有的定价工作基于抽象的凹效用函数，不依赖于物理层参数。为此，我们首先引入了一个新的效用函数，与在本地执行作业相比，它通过卸载来衡量成本降低。基于这个效用函数，我们然后制定了两个卸载游戏，一个最大化个人兴趣，另一个最大化整个系统的兴趣。我们分析了博弈的结构特性，并以封闭形式承认了同质用户案例的纳什均衡和社会均衡，分别。所提出的表达式是用户参数的函数，例如时间和能量的权重、与 AP 的距离，从而构成对先前仅考虑抽象函数的经济工作的进步。最后，我们提出了一个最优的基于价格的方案，我们证明了与自利用户的交互决策过程收敛到一个与社会均衡点相等的纳什均衡点。