Edge Computing (EC) offers a superior user experience by positioning cloud resources in close proximity to end users. The challenge of allocating edge resources efficiently while maximizing profit for the EC platform remains a sophisticated problem, especially with the added complexity of the online arrival of resource requests. To address this challenge, we propose to cast the problem as a multi-armed bandit problem and develop two novel online pricing mechanisms, the Kullback-Leibler Upper Confidence Bound (KL-UCB) algorithm and the Min-Max Optimal algorithm, for heterogeneous edge resource allocation. These mechanisms operate in real-time and do not require prior knowledge of demand distribution, which can be difficult to obtain in practice. The proposed posted pricing schemes allow users to select and pay for their preferred resources, with the platform dynamically adjusting resource prices based on observed historical data. Numerical results show the advantages of the proposed mechanisms compared to several benchmark schemes derived from traditional bandit algorithms, including the Epsilon-Greedy, basic UCB, and Thompson Sampling algorithms.

翻译：边缘计算通过将云端资源部署在靠近终端用户的位置，显著提升了用户体验。然而，在满足在线资源请求到达的复杂约束下，如何高效分配边缘资源并最大化边缘计算平台利润仍是一个亟待解决的难题。针对这一挑战，我们提出将问题建模为多臂赌博机问题，并开发了两种面向异质边缘资源分配的在线定价机制：KL-UCB算法与最小-最大最优算法。这两种机制可实时运行，且无需预先获取实践中难以获得的资源需求分布信息。所提出的标价定价方案允许用户自主选择并支付偏好资源，平台则根据历史观测数据动态调整资源价格。数值结果表明，相较于基于传统赌博机算法（包括Epsilon-Greedy、基础UCB及汤普森采样算法）的多种基准方案，本文提出的机制具有显著优势。