Dynamic adaptive techniques for learning application delay tolerance for mobile data offloading

Abstract

Today's worldwide mobile data traffic is roughly 18× larger than the full internet traffic in 2000, and continued large growth is expected. High mobile data usage has implications both for users and providers. For individual users, relying on cellular data connectivity incurs high cellular data fees. For cellular network providers, high mobile data usage requires expensive, ongoing infrastructure upgrades. Cellular data usage can be reduced by offloading to WiFi when available. If not available, prior work has considered delaying transmissions to wait for WiFi availability. While exploiting such application delay tolerance offers significant energy and performance leverage for data offloading and other techniques, a key question is: how long to wait? Prior work does not discuss how to estimate application delay tolerance without explicit help from programmers, nor how to adjust the estimate dynamically. This work proposes, implements, and evaluates four schemes to dynamically and adaptively deduce an application's delay tolerance. These schemes (Adaptive, Decision Tree-Based, Hybrid and Lazy) are low-overhead and effective. In our experiments, they cut cellular usage by 2× or more compared to non-delay-tolerant approaches. Furthermore, our dynamically adaptive decision schemes achieve up to 15% further cellular data reduction compared to fixed static delay tolerance values.