Similarity caching allows requests for an item to be served by a similar item. Applications include recommendation systems, multimedia retrieval, and machine learning. Recently, many similarity caching policies have been proposed, like SIM-LRU and RND-LRU, but the performance analysis of their hit rate is still wanting. In this paper, we show how to extend the popular time-to-live approximation in classic caching to similarity caching. In particular, we propose a method to estimate the hit rate of the similarity caching policy RND-LRU. Our method, the RND-TTL approximation, introduces the RND-TTL cache model and then tunes its parameters in such a way to mimic the behavior of RND-LRU. The parameter tuning involves solving a fixed point system of equations for which we provide an algorithm for numerical resolution and sufficient conditions for its convergence. Our approach for approximating the hit rate of RND-LRU is evaluated on both synthetic and real world traces.

翻译：相似性缓存允许通过相似项来服务于某个项的请求。其应用包括推荐系统、多媒体检索和机器学习。近年来，尽管已提出许多相似性缓存策略（如SIM-LRU和RND-LRU），但其命中率的性能分析仍显不足。本文展示了如何将经典缓存中流行的生存时间近似方法扩展至相似性缓存。具体而言，我们提出了一种用于估计相似性缓存策略RND-LRU命中率的方法。该方法（即RND-TTL近似）引入了RND-TTL缓存模型，并通过调整其参数以模拟RND-LRU的行为。参数调整涉及求解一个不动点方程组，我们提供了该方程组的数值求解算法及其收敛的充分条件。我们在合成轨迹和真实轨迹上对所提出的RND-LRU命中率近似方法进行了评估。