Abstract
In cellular networks, tail states are designed for a tradeoff between energy efficiency and latency. However, the energy consumed during tail states becomes a huge energy drainer itself. Traditional energy saving techniques by content prefetching cannot be directly applied to mobile ads, due to the deadline requirements of ads, randomness in user behaviors, different usage patterns of mobile apps and system services. In this paper, considering several significant runtime factors, we make a novel use of Markov Decision Process to model the energy minimization problem for ad prefetching (EMAP), and propose an algorithmic solution to the EMAP problem. Further, we implement the first mobile ad prefetching system that is fully compatible with contemporary ad libraries and mobile apps. By replaying real-world user traces on Android devices, we show our proposed solution consistently outperforms existing On-Demand policy on Android by up to 59% in saving ad-related energy, while a simple Fill-Up-Buffer policy can be even 2 times worse than the default On-Demand policy. Such findings provide critical insights regarding the promise of saving energy by ad prefetching in real-world mobile systems.
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