On Optimal Proactive Caching for Mobile Networks With Demand Uncertainties

Mobile data users are known to possess predictable characteristics both in their interests and activity patterns. Yet, their service is predominantly performed, especially at the wireless edges, "reactively" at the time of request, typically when the network is under heavy traffic load. This strategy incurs excessive costs to the service providers to sustain on-time (or delay-intolerant) delivery of data content, while their resources are left underutilized during the light-loaded hours. This motivates us in this work to study the problem of optimal "proactive" caching whereby, future delay-intolerant data demands can be served within a given prediction window ahead of their actual time-of-arrival to minimize service costs. To that end, we first establish fundamental bounds on the minimum possible cost achievable by any proactive policy, as a function of the prediction uncertainties. These bounds provide interesting insights on the impact of uncertainty on the maximum achievable proactive gains. We then propose specific proactive caching strategies, both for uniform and fluctuating demand patterns, that are asymptotically-optimal in the limit as the prediction window size grows while the prediction uncertainties remain fixed. We further establish the exponential convergence rate characteristics of our proposed solutions to the optimal, revealing close-to-optimal performance characteristics of our designs even with small prediction windows. Also, proactive design is contrasted with its reactive and delay-tolerant counter-parts to obtain interesting results on the unavoidable costs of uncertainty and the potentially remarkable gains of proactive operation.