Minimizing capacity requirements of cellular networks via delayed scheduling

The volume of data in broadband cellular network is growing exponentially. However, studies have indicated the traffic load on the cellular base stations varies significantly over time. This gives an opportunity to accommodate additional traffic with the same network capacity if some of the traffic (e.g., p2p, cloud sync) can be amenable to 'delayed scheduling' without hurting the user experience any significantly. In this paper, we study various algorithmic problems that can arise in this context. Using a model where all flows can have certain flexibility in scheduling (via use of a 'deadline'), we develop optimal or near-optimal algorithms to determine the minimum network capacity for two different models. We also develop various semi-online and online algorithms for online scheduling of flows, and analyze their performance. In particular, even though the online scheduling problem is shown to be intractable, our proposed semi-online algorithm can schedule flows optimally if aided by historical data and slightly additional network capacity over the optimal. Finally, using flow level traffic traces collected at the core of a commercially operated cellular network, we evaluate the effectiveness of our techniques. Evaluations show that delayed scheduling, when done efficiently (using an offline optimal algorithm), can accommodate the same traffic with much lower network capacity (up to 50% less) with only modest delays. While such an optimal solution needs an offline approach, we demonstrate that online scheduling can be almost equally effective when historical traffic data can be exploited for estimation purposes.