Performance evaluation of hierarchical TTL-based cache networks

There has been considerable research on the performance analysis of on-demand caching replacement policies like Least-Recently-Used (LRU), First-In-First-Out (FIFO) or Random (RND). Much progress has been made on the analysis of a single cache running these algorithms. However it has been almost impossible to extend the results to networks of caches. In this paper, we introduce a Time-To-Live (TTL) based caching model, that assigns a timer to each content stored in the cache and redraws it every time the content is requested (at each hit/miss). We derive the performance metrics (hit/miss ratio and rate, occupancy) of a TTL-based cache in isolation fed by stationary and ergodic request processes with general TTL distributions. Moreover we propose an iterative procedure to analyze TTL-based cache networks under the assumptions that requests are described by renewal processes   (that generalize Poisson processes or the standard IRM assumption). We validate our theoretical findings through event-driven and Monte-Carlo simulations based on the Fourier Amplitude Sensitivity Test to explore the space of the input parameters. We observe that our analytic model predicts remarkably well all metrics of interest with relative errors smaller than 1%1%.