On Optimal Proactive Caching for Mobile Networks With Demand Uncertainties

Abstract

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.Index Terms-predictable demand, proactive caching, resource allocation, scheduling, uncertainty. 1063-6692

LAPRA: Location-Aware Proactive Resource Allocation

“…This is expected as with increase in , it is always impossible to satisfy all users demand requirements. The reactive approach is most sensitive 2 At each location, maximum rate is chosen from the rates provided by each AP. to increase in : sat starts decreasing after = 13 bits/sec/Hz.…”

“…We can classify resource allocation as either reactive or proactive, depending on whether they are able to harness the user's future characteristics, such as demand patterns, behavior, and channel characteristics. Predictability of demand patterns is considered in the context of proactive caching in [2]. In [3], a multi-user rate allocation method is proposed based on predicted user rates for efficient energy transmission of stored videos that can be cached at the user devices.…”

On proactive caching with demand and channel uncertainties

“…In contrast, proactive resource allocation strategies, which are seen as one of the key disruptive technologies for 5G wireless networks [9], can track, learn, and then predict the user demand requests ahead of time, and hence possess more flexibility in scheduling these requests before their actual time of arrival. The main advantage of this approach is network load balancing over large time scale dynamics, at the expense of possible waste of network resources [10].…”

“…It provided a solid theoretical background and demonstrated significant spectral efficiency gains in various scenarios. In [10], proactive resource allocation schemes under time-invariant and time-varying demand statistics are studied. The authors proposed fundamental lower bounds on the achievable costs, and developed asymptotically optimal policies that approach these bounds when the prediction window size is increased.…”

“…• We extend the work in [10] with the inclusion of predicted channel statistics of the users and study its effect on the proactive scheduling. • We establish a global lower bound on the proactive scheduling cost that captures the impact of demand and channel uncertainties.…”