Demystifying Traffic Statistics for Edge Cache Deployment in Large-Scale WiFi System

How to deploy cache in large-scale WiFi system is not well studied yet quite challenging since numerous Aps turn to be heterogeneous in terms of traffic consumption, and future traffic conditions are unknown ahead. In this paper, given the cache storage budge, we explore the cache deployment in a large-scale WiFi system which contains 8,000 APs and serves more than 40,000 active users, to maximize the long-term caching gain, i.e., the total reduced backhaul traffic. Specifically, we first collect enormous user association records and conduct intensive statistical analysis on the collected data, gaining two major observations. First, per AP traffic consumption varies in a rather wide range and the AP proportion distributes evenly within the range, which indicates that the cache size should be heterogeneously allocated in accordance to the underlying traffic demands. Second, compared to a single AP, the traffic consumption of a group of APs (clustered by physical locations) is more stable, which means that the short-term traffic statistics can be used to infer the future long-term traffic conditions. We then propose our cache deployment strategy, named LEAD (i.e., Large-scale wifi Edge cAche Deployment), in which we first cluster large-scale APs into well-sized edge nodes, then conduct the stationary testing on edge level traffic consumption and sample sufficient traffic statistics in order to precisely characterize future traffic conditions, and finally devise the TEG (Traffic-wEighted Greedy) algorithm to solve the long-term caching gain maximization problem. Extensive trace-driven simulations are carried out and simulation results demonstrate the efficacy of LEAD.