Data caching is an effective method to reduce traffic and improve the quality of service in network. Traditionally, users' requests are offloaded to the cloud for centralized computing. However, due to security and privacy, these tasks are executed in the nearest server, so that the data and service needed by the task are also essential. After the task is completed, in case the next arriving request needs the same data, resulting in transmission cost, the data need to be stored for a period of time, because we know nothing about the coming request information under an online request stream. In this article, we study data caching problem by extending single data item to multiple data items among servers. About the homogeneous model and the submodular model with constraint, we propose a data caching strategy minimizing the total transfer and caching costs of the system. Moreover, we also solve the semiheterogeneous model by the anticipatory caching (AC) algorithm in Reference 21. Meanwhile we find it is more efficient for our three models in this article to improve the performance.

中文翻译：

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边缘计算中的在线数据缓存

数据缓存是减少网络流量、提高网络服务质量的有效方法。传统上，用户的请求被卸载到云端进行集中计算。但出于安全和隐私的考虑，这些任务都是在就近的服务器中执行，因此任务所需的数据和服务也是必不可少的。任务完成后，如果下一个到达的请求需要相同的数据，从而产生传输成本，则需要将数据存储一段时间，因为我们对在线请求流下即将到来的请求信息一无所知。在本文中，我们通过将单个数据项扩展到服务器之间的多个数据项来研究数据缓存问题。关于齐次模型和带约束的子模模型，我们提出了一种数据缓存策略，最大限度地减少系统的总传输和缓存成本。此外，我们还在参考文献21中通过预期缓存（AC）算法解决了半异构模型。同时我们发现本文中的三个模型在提高性能方面更有效。