Edge computing has emerged as a new computing paradigm that allows computation and storage resources in the cloud to be distributed to edge servers. Those edge servers are deployed at base stations to provide nearby users with high-quality services. Thus, data caching is extremely important in ensuring low latency for service delivery in the edge computing environment. To minimize the data caching cost and maximize the reduction in service latency, we formulate this Edge Data Caching (EDC) problem as a constrained optimization problem in this paper. We prove the $\mathcal{NP}$-completeness of this EDC problem and provide an optimal solution named IPEDC to solve this problem based on Integer Programming. Then, we propose an approximation algorithm named AEDC to find approximate solutions with a limited bound. We conduct intensive experiments on a real-world data set and a synthesized data set to evaluate our approaches. Our results demonstrate that IPEDC and AEDC significantly outperform the four representative baseline approaches.

边缘计算已经成为一种新的计算范式，它允许将云中的计算和存储资源分发到边缘服务器。这些边缘服务器部署在基站上，以向附近的用户提供高质量的服务。因此，数据缓存对于确保边缘计算环境中服务交付的低延迟非常重要。为了最小化数据缓存成本并最大程度地减少服务等待时间，我们在本文中将边缘数据缓存（EDC）问题表述为约束优化问题。我们证明$\mathcal{NP}$-EDC问题的完全性，并提供一个名为IPEDC的最佳解决方案以基于整数编程解决此问题。然后，我们提出一种称为AEDC的近似算法，以找到有限范围内的近似解。我们对真实数据集和综合数据集进行了密集的实验，以评估我们的方法。我们的结果表明，IPEDC和AEDC明显优于四种代表性基线方法。