Optimal Edge Caching For Individualized Demand Dynamics

The ever-growing end user data demands, and the reductions in memory costs are fueling edge-caching deployments. Caching at the edge is substantially different from that at the core and needs to consider the nature of individualized data demands. For example, an individual user may not be interested in requesting the same data item again, if it has recently requested it. Such individualized dynamics are not apparent in the aggregated data requests at the core and have not been considered in popularity-driven caching designs for the core. Hence, these traditional caching policies could induce significant inefficiencies when applied at the edges. To address this issue, we develop new edge caching policies optimized for the individualized demands that also leverage overhearing opportunities at the wireless edge. With the objective of maximizing the hit ratio, the proposed policies will actively evict the data items that are not likely to be requested in the near future, and strategically bring them back into the cache via overhearing when they become popular again. Both theoretical analysis and numerical simulations demonstrate that the proposed edge caching policies could outperform the popularity-driven policies that are optimal at the core.