Single vs Distributed Edge Caching for Dynamic Content

Existing content caching mechanisms are predominantly geared towards easy-access to content that is static once created. However, numerous applications, such as news and dynamic sources with time-varying states, generate ‘dynamic’ content where new updates replace previous versions. This motivates us in this work to study the freshness-driven caching algorithm for dynamic content, which accounts for the changing nature of data content. In particular, we provide new models and analyses of the average operational cost both for the single and distributed edge caching scenarios. In both scenarios, we characterize the performance of the optimal solution and develop algorithms to select the content and the update rate that the user(s) must employ to have low-cost access to fresh content. Moreover, our work reveals new and easy-to-calculate key metrics for quantifying the caching value of dynamic content in terms of their refresh rates, popularity, number of users in the distribute edge caching group, and the fetching and update costs associated with the optimal decisions. We compare the proposed freshness-driven caching strategies with benchmark caching strategies like cache the most popular content. Results demonstrate that freshness-driven caching strategies considerably enhance the utilization of the edge caches with possibly orders-of-magnitude cost reduction. Furthermore, our investigations reveal that the distributed edge caching scenario, benefiting from the multicasting property of wireless service to update the cached content, can be cost-effective compared to the single edge caching, as the number of edge caches increases.