Optimal Load-Splitting and Distributed-Caching for Dynamic Content

In this work, we consider the problem of ‘fresh’ caching at distributed (front-end) local caches of content that is subject to ‘dynamic’ updates at the (back-end) database. We first provide new models and analyses of the average operational cost of a network of distributed edge-caches that utilizes wireless multicast to refresh aging content. We attack the problems of what to cache in each edge-cache and how to split the incoming demand amongst them (also called "loadsplitting" in the rest of the paper) in order to minimize the operational cost. While the general form of the problem comes with an NP-hard Knapsack structure, we were able to completely solve the problem by judiciously choosing the number of edge-caches to be deployed over the network. Interestingly, our findings reveal that the optimal caching policy necessitates unequal load-splitting over the edge-caches even when all conditions are symmetric. Moreover, we find that edge- caches with higher load will generally cache fewer but relatively more popular content. We further investigate the tradeoffs between cost reduction and cache savings when employing equal and optimal load-splitting solutions for demand with Zipf(z) popularity distribution. Our analysis reveals that equal load-splitting to edge-caches achieves close-to-optimal for less predictable demand (z < 2) while also saving in the cache size. On the other hand, for more predictable demand (z > 2), optimal load-splitting results in substantial cost gains while decreasing the cache occupancy.