Energy-delay tradeoffs in smartphone applications
Energy-delay tradeoffs in smartphone applications
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DOI：
10.1145/1814433.1814459
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发表时间：
2010-06
期刊：
影响因子：
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通讯作者：
Moo-Ryong Ra;Jeongyeup Paek;Abhishek B. Sharma;Ramesh Govindan;Martin H. Krieger;M. Neely
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文献类型：
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作者：
Moo-Ryong Ra;Jeongyeup Paek;Abhishek B. Sharma;Ramesh Govindan;Martin H. Krieger;M. Neely
许多应用程序通过在智能手机上捕获视频并将这些视频上传到连接互联网的服务器的能力来实现。此功能需要将大量数据从电话传输到基础设施。智能手机有多种无线接口-- 3G/EDGE和WiFi --用于数据传输，但这些网络的可用性和可实现的数据传输速率存在相当大的差异。此外，在这些无线接口上传输给定量数据的能量成本可能相差一个数量级。另一方面，这些应用程序中的许多通常是自然的延迟容忍，因此可以延迟数据传输，直到较低能耗的WiFi连接可用。在本文中，我们提出了一个原则性的方法，设计一个最佳的在线算法，这种能量延迟权衡使用李雅普诺夫优化框架。我们的算法，称为SALSA，可以自动适应信道条件，只需要本地信息来决定是否以及何时推迟传输。我们评估SALSA使用现实世界的痕迹，以及使用现代智能手机上的原型实现的实验。我们的研究结果表明，SALSA可以进行调整，以实现广泛的能量延迟权衡，比我们比较的任何替代方案都更接近于精确确定的最优值，并且可以为某些工作负载节省10-40%的电池容量。
Many applications are enabled by the ability to capture videos on a smartphone and to have these videos uploaded to an Internet-connected server. This capability requires the transfer of large volumes of data from the phone to the infrastructure. Smartphones have multiple wireless interfaces -- 3G/EDGE and WiFi -- for data transfer, but there is considerable variability in the availability and achievable data transfer rate for these networks. Moreover, the energy costs for transmitting a given amount of data on these wireless interfaces can differ by an order of magnitude. On the other hand, many of these applications are often naturally delay-tolerant, so that it is possible to delay data transfers until a lower-energy WiFi connection becomes available. In this paper, we present a principled approach for designing an optimal online algorithm for this energy-delay tradeoff using the Lyapunov optimization framework. Our algorithm, called SALSA, can automatically adapt to channel conditions and requires only local information to decide whether and when to defer a transmission. We evaluate SALSA using real-world traces as well as experiments using a prototype implementation on a modern smartphone. Our results show that SALSA can be tuned to achieve a broad spectrum of energy-delay tradeoffs, is closer to an empirically-determined optimal than any of the alternatives we compare it to, and, can save 10-40% of battery capacity for some workloads.