Statistical Approximation of Efficient Caching Mechanisms for One-Timers

With the increasing diversity of network services and applications, caching technologies for content delivery networks (CDNs) and information-centric networking (ICN) have become beneficial to improve the service quality such as communication latency by storing content data on network nodes near to users. Cache performance depends on the memory size at each node as well as the request distribution of content, and it becomes an important issue to analyze the performance of current caching mechanisms for designing efficient cache systems. In general, the request distribution in content dissemination networks is heavy-tailed, containing many objects called one-timers, which are accessed only once and are not hit while in the cache. Moreover, one-timers may diminish the whole cache performance by causing inefficient cache replacement of more popular content. To solve this problem, effective caching mechanisms having separate queues for one-timers, such as 2Q and adaptive replacement caching (ARC), have been proposed. In this paper, we focus on analyzing the 2Q and ARC mechanisms and propose approximation models that can statistically analyze the influence of memory size and request distribution on the cache performance. Furthermore, we evaluate the accuracy of both approximation models.