Stochastic energy optimization for mobile GPS applications
Stochastic energy optimization for mobile GPS applications
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DOI：
10.1145/3236024.3236076
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发表时间：
2018-10
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影响因子：
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通讯作者：
Anthony Canino;Yu David Liu;Hidehiko Masuhara
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作者：
Anthony Canino;Yu David Liu;Hidehiko Masuhara
移动应用程序定期与他们的噪音和不断变化的物理环境进行互动。在不确定性的情况下，Android提供了一种简约的编程模型旋钮和特定应用的优化目标（例如满足能源预算）是奖励的，这两种都可以直接编程，埃涅阿斯具有随机优化器，以适应性地选择奖励优美的旋钮。一种增强性学习的形式。覆盖大约6500英里和150小时的驾驶以及20小时的骑自行车和远足，我们发现在不确定的物理环境中，各个GPS读数都可以有效，有弹性地符合程序员指定的能量预算，而与非策略相比诸如配置引导优化之类的方法，EENEAS在整个运行中产生更明显的稳定结果。
Mobile applications regularly interact with their noisy and ever-changing physical environment. The fundamentally uncertain nature of such interactions leads to significant challenges in energy optimization, a crucial goal of software engineering on mobile devices. This paper presents Aeneas, a novel energy optimization framework for Android in the presence of uncertainty. Aeneas provides a minimalistic programming model where acceptable program behavioral settings are abstracted as knobs and application-specific optimization goals — such as meeting an energy budget — are crystallized as rewards, both of which are directly programmable. At its heart, Aeneas is endowed with a stochastic optimizer to adaptively and intelligently select the reward-optimal knob setting through a form of reinforcement learning. We evaluate Aeneas on mobile GPS applications built over Google LocationService API. Through an in-field case study that covers approximately 6500 miles and 150 hours of driving as well as 20 hours of biking and hiking, we find that Aeneas can effectively and resiliently meet programmer-specified energy budgets in uncertain physical environments where individual GPS readings undergo significant fluctuation. Compared with non-stochastic approaches such as profile-guided optimization, Aeneas produces significantly more stable results across runs.