A Fine-grained Event-based Modem Power Model for Enabling In-depth Modem Energy Drain Analysis
A Fine-grained Event-based Modem Power Model for Enabling In-depth Modem Energy Drain Analysis
复制标题
基于事件的细粒度调制解调器功率模型，可实现深入的调制解调器能量消耗分析
DOI：
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发表时间：
2017
期刊：
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通讯作者：
Ashish Singh
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文献类型：
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作者：
Xiaomeng Chen;Jiayi Meng;HU Y.CHARLIE;Maruti Gupta;Ralph Hasholzner;Venkatesan N. Ekambaram;Ashish Singh
蜂窝调制解调器使无处不在的互联网连接到现代智能手机，但这样做，他们成为智能手机能源消耗的主要贡献者。了解调制解调器的能量消耗需要详细的功率模型。现有技术，即基于RRC状态的功率模型，主要被开发来对应用数据传输的调制解调器能量消耗进行建模。因此，它很好地服务于其最初的目的，但不足以研究详细的现代行为，例如，控制平面上的活动。在本文中，我们提出了一种新的方法，在事件粒度建模调制解调器功耗行为，并开发我们所知的第一个细粒度的调制解调器功率模型，捕获在不同的RRC模式下的所有LTE调制解调器无线电事件的功率消耗。其次，我们定量地证明了新模型的优势，基于状态的功率模型在各种各样的情况下，通过控制实验。最后，使用我们的细粒度调制解调器功率模型，我们进行了第一次详细的调制解调器能量消耗在野外研究，涉及12个Nexus 6手机在正常使用下，由12名志愿者跨越共348天。我们的研究提供了第一个定量分析的能源消耗，由于在野外的现代控制活动，并暴露其相关性与上下文，如位置和用户的移动性。
Cellular modems enable ubiquitous Internet connectivities to modern smartphones, but in doing so they become a major contributor to the smartphone energy drain. Understanding modem energy drain requires a detailed power model. The prior art, an RRC-state based power model, was developed primarily to model the modem energy drain of application data transfer. As such, it serves well its original purpose, but is insufficient to study detailed modem behavior, e.g., activities in the control plane. In this paper, we propose a new methodology of modeling modem power draw behavior at the event-granularity, and develop to our knowledge the first fine-grained modem power model that captures the power draw of all LTE modem radio-on events in different RRC modes. Second, we quantitatively demonstrate the advantages of the new model over the state-based power model under a wide variety of context via controlled experiments. Finally, using our fine-grained modem power model, we perform the first detailed modem energy drain in-the-wild study involving 12 Nexus 6 phones under normal usage by 12 volunteers spanning a total of 348 days. Our study provides the first quantitative analysis of energy drain due to modem control activities in the wild and exposes their correlation with context such as location and user mobility.