Caching with Time Domain Buffer Sharing

TL;DR

This paper introduces a caching strategy based on time domain buffer sharing that optimizes storage efficiency and hit ratio by predicting request delays and managing buffer resources, applicable to both infinite and finite buffer scenarios.

Contribution

It develops queueing theoretic models for caching with infinite and finite buffers, incorporating request delay prediction and storage cost optimization to enhance caching performance.

Findings

Optimal hit ratio is limited by demand probability and buffer size.

Diffusion approximation and Erlang-B formula effectively estimate buffer overflow probability.

Probabilistic caching with random maximum caching time improves content harvesting without needing request statistics.

Abstract

In this paper, storage efficient caching based on time domain buffer sharing is considered. The caching policy allows a user to determine whether and how long it should cache a content item according to the prediction of its random request time, also referred to as the request delay information (RDI). In particular, the aim is to maximize the caching gain for communications while limiting its storage cost. To achieve this goal, a queueing theoretic model for caching with infinite buffers is first formulated, in which Little's law is adopted to obtain the tradeoff between the hit ratio and the average buffer consumption. When there exist multiple content classes with different RDIs, the storage efficiency is further optimized by carefully allocating the storage cost. For more practical finite-buffer caching, a $G/GI/L/0$ queue model is formulated, in which a diffusion approximation and Erlang-B formula are adopted to determine the buffer overflow probability and the corresponding hit ratio. The optimal hit ratio is shown to be limited by the demand probability and buffer size for large and small buffers respectively. In practice, a user may exploit probabilistic caching with random maximum caching time and arithmetic caching without any need for content arrival statistics to efficiently harvest content files in air.