# Distributed Network Caching via Dynamic Programming

## TL;DR

This paper develops optimal caching strategies for distributed networks with local storage, using dynamic programming to minimize costs considering content popularity, time, and location dynamics, aiming to improve network efficiency and user satisfaction.

## Contribution

It introduces a novel optimization framework for distributed caching that accounts for spatio-temporal content dynamics and proposes a dynamic programming-based solution with reduced complexity.

## Key findings

- The proposed algorithm effectively balances cache placement costs across space and time.
- Simulation results demonstrate improved caching efficiency and cost reduction.
- The framework adapts to changing content popularities and network conditions.

## Abstract

Next-generation communication networks are envisioned to extensively utilize storage-enabled caching units to alleviate unfavorable surges of data traffic by pro-actively storing anticipated highly popular contents across geographically distributed storage devices during off-peak periods. This resource pre-allocation is envisioned not only to improve network efficiency, but also to increase user satisfaction. In this context, the present paper designs optimal caching schemes for \textit{distributed caching} scenarios. In particular, we look at networks where a central node (base station) communicates with a number of "regular" nodes (users or pico base stations) equipped with \textit{local storage} infrastructure. Given the spatio-temporal dynamics of content popularities, and the decentralized nature of our setup, the problem boils down to select what, when and \textit{where} to cache. To address this problem, we define fetching and caching prices that vary across contents, time and space, and formulate a global optimization problem which aggregates the costs across those three domains. The resultant optimization is solved using decomposition and dynamic programming techniques, and a reduced-complexity algorithm is finally proposed. Preliminary simulations illustrating the behavior of our algorithm are finally presented.