This paper focuses on similarity caching systems, in which a user request for an {object~$o$} that is not in the cache can be (partially) satisfied by a similar stored {object~$o'$}, at the cost of a loss of user utility. Similarity caching systems can be effectively employed in several application areas, like multimedia retrieval, recommender systems, genome study, and machine learning training/serving. However, despite their relevance, the behavior of such systems is far from being well understood. In this paper, we provide a first comprehensive analysis of similarity caching in the offline, adversarial, and stochastic settings. We show that similarity caching raises significant new challenges, for which we propose the first dynamic policies with some optimality guarantees. We evaluate the performance of our schemes under both synthetic and real request traces.

翻译：本文侧重于相似的缓存系统,其中用户对不在缓存中的 {ject~$$} 的请求可以(部分)由类似的存储 {oject~$$} 满足,以失去用户效用为代价。相似的缓存系统可以在多个应用领域有效使用,如多媒体检索、建议系统、基因组研究以及机器学习培训/服务。然而,尽管这些系统具有相关性,但其行为远未得到很好地理解。在本文件中,我们提供了对离线、对立和随机环境的类似缓存的首次全面分析。我们表明,相似的缓存提出了新的重大挑战,为此我们提出了第一个动态政策,并提供了一些最佳的保证。我们评估了我们的计划在合成和真实请求追踪下的执行情况。