SceneMan: Bridging mobile apps with system energy manager via scenario notification
SceneMan: Bridging mobile apps with system energy manager via scenario notification
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SceneMan：通过场景通知将移动应用程序与系统能源管理器桥接
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Ziang Hu
中科院分区：
文献类型：
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作者：
Li Li;J. Wang;Xiaorui Wang;Handong Ye;Ziang Hu
当前移动设备上的电源管理依赖于称为DVFS调控器的操作系统模块。但是，现有的调控器仅基于CPU负载等低级信息来确定系统配置，而没有任何关于应用程序级行为的输入。特别是，没有从移动应用程序到能源管理器的通信。我们发现有关应用程序使用场景的信息（例如，游戏，视频聊天）通常可以帮助能源管理器更好地完成工作并实现更多的节能。虽然已经提出了应用程序级别的能源优化，但它们通常侧重于单一使用场景，而不是跨多个场景的优化。在本文中，我们提出了SceneMan，一个基于使用场景通知的移动应用的能源优化框架。SceneMan有三个组件：一个API、一个场景通知器和一个能源管理器。关键思想是让能源管理人员意识到应用程序级别的场景。在运行时，应用程序通过提供的开发人员使用的api通知能源管理器它们的使用场景。然后，能源管理器采取适当的行动，在满足性能要求的同时，将运行场景的能耗降至最低。因此，跨场景的能源优化可以很容易地实现。该框架几乎不需要额外的编程工作，可以帮助应用程序以透明的方式实现更好的能源效率。我们在Nexus 6智能手机上实现了我们的系统，并在游戏和视频聊天两种使用场景下用13个真实世界的应用程序进行了测试。我们实现了高达33.2%的节能，最坏情况下的性能损失为5.1%。
Power management on current mobile devices relies on OS modules known as DVFS governors. However, existing governors determine system configuration only based on low-level information such as CPU load without any input about application-level behaviors. In particular, there exists no communication from mobile apps to energy managers. We find that information about app usage scenarios (e.g., gaming, video chatting) can usually help energy manager perform a better job and achieve more energy savings. Although app-level energy optimizations have been proposed, they generally focus on single usage scenarios and do not address optimization across multiple scenarios. In this paper, we propose SceneMan, an energy optimization framework for mobile apps based on usage scenario notification. SceneMan has three components: an API, a scenario notifier, and an energy manager. The key idea is to make energy managers aware of app-level scenarios. At runtime, apps notify the energy manager about their usage scenarios with provided APIs used by developers. The energy manager then takes appropriate actions to minimize energy consumption of the running scenario while meeting performance requirements. Energy optimization across scenarios can thus be easily achieved. The framework requires little extra programming effort and can help apps achieve better energy efficiency in a transparent way. We implement our system on a Nexus 6 smartphone and test it with 13 real-world apps under 2 usage scenarios, namely, gaming and video chatting. We achieve up to 33.2% energy savings with a worst-case performance loss of 5.1%.