Optimal Content Caching and Recommendation with Age of Information

TL;DR

This paper addresses the joint optimization of content caching, recommendation, and freshness management at network edges, proposing algorithms for NP-hard scheduling problems to improve user experience and system efficiency.

Contribution

It introduces a novel joint framework for cache update scheduling considering content recommendation and age of information, with algorithms for small-scale optimality and large-scale approximation.

Findings

The proposed algorithm outperforms greedy scheduling in simulations.

The problem is proven NP-hard, highlighting its computational complexity.

Efficient algorithms provide performance bounds for cache update strategies.

Abstract

Content caching at the network edge has been considered an effective way of mitigating backhaul load and improving user experience. Caching efficiency can be enhanced by content recommendation and by keeping the information fresh. To the best of our knowledge, there is no work that jointly takes into account these aspects. By content recommendation, a requested content that is not in the cache can be alternatively satisfied by a related cached content recommended by the system. Information freshness can be quantified by age of information (AoI). We address, optimal scheduling of cache updates for a time-slotted system accounting for content recommendation and AoI. For each content, there are requests that need to be satisfied, and there is a cost function capturing the freshness of information. We present the following contributions. First, we prove that the problem is NP-hard. Second, we derive an integer linear formulation, by which the optimal solution can be obtained for small-scale scenarios. Third, we develop an algorithm based on Lagrangian decomposition. Fourth, we develop efficient algorithms for solving the resulting subproblems. Our algorithm computes a bound that can be used to evaluate the performance of any suboptimal solution. Finally, we conduct simulations to show the effectiveness of our algorithm in comparison to a greedy schedule.