As an emerging computing paradigm, edge computing offers computing resources closer to the data sources, helping to improve the service quality of many real-time applications. A crucial problem is designing a rational pricing mechanism to maximize the revenue of the edge computing service provider (ECSP). However, prior works have considerable limitations: clients are static and are required to disclose their preferences, which is impractical in reality. However, previous works assume user privacy information to be known or consider the number of users in edge scenarios to be static. To address this issue, we propose a novel sequential computation offloading mechanism, where the ECSP posts prices of computing resources with different configurations to clients in turn. Clients independently choose which computing resources to purchase and how to offload based on their prices. Then Egret, a deep reinforcement learning-based approach that achieves maximum revenue, is proposed. Egret determines the optimal price and visiting orders online without considering clients' preferences. Experimental results show that the revenue of ECSP in Egret is only 1.29\% lower than Oracle and 23.43\% better than the state-of-the-art when the client arrives dynamically.

翻译：作为一种新兴的计算范式，边缘计算在更靠近数据源的位置提供计算资源，有助于提升众多实时应用的服务质量。关键问题之一在于设计合理的定价机制，以最大化边缘计算服务提供商（ECSP）的收益。然而，现有工作存在显著局限：客户端是静态的，且需要披露其偏好，这在实际应用中不切实际。此外，先前的研究假设用户隐私信息已知，或认为边缘场景中的用户数量是静态的。为解决这一问题，我们提出了一种新颖的顺序计算卸载机制，其中ECSP依次向客户端发布不同配置计算资源的价格。客户端独立选择购买何种计算资源，并根据价格决定如何卸载任务。进而，我们提出了Egret——一种基于深度强化学习的方法，能够实现最大收益。Egret在不考虑客户端偏好的情况下在线确定最优价格和访问顺序。实验结果表明，在客户端动态到达的场景下，采用Egret的ECSP收益仅比Oracle低1.29%，且比现有最优方法高23.43%。