Proactive retention aware caching

We consider the problem of proactive (i.e. predictive) content caching that is aware of the costs of retention of the content in the cache. Prior work on caching (whether proactive or reactive) does not explicitly take into account the storage cost due to the duration of time for which a content is cached. This new problem, which we call retention aware caching, is motivated by two recent technological developments that are described in the paper: cloud storage rental costs and flash memory damage. We consider a hierarchical network consisting of a server connected to a number of cache-enabled nodes, located either at the edge of a network (e.g. base stations) or in the core of a data center. There are two types of network costs: storage cost at the caches and download cost from the server. We formulate the problem of proactive retention aware data caching (PRAC), which minimizes the total cost subject to the node capacity constraints. We first prove that PRAC is NP-Hard in general and then analyze PRAC for two cases: (1) linear storage cost, (2) convex storage cost. We show that PRAC admits efficient polynomial time algorithms when the storage cost is linear in retention times and caches have a large capacity. Furthermore, we derive bounds on the performance of PRAC for the case when the storage cost is a practically motivated convex function. Numerical evaluations demonstrate that PRAC outperforms other state-of-the-art caching policies for a wide range of parameters of interest.