REHASH: A Flexible, Developer Focused, Heuristic Adaptation Platform for Intermittently Powered Computing
REHASH: A Flexible, Developer Focused, Heuristic Adaptation Platform for Intermittently Powered Computing
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REHASH：一个灵活的、以开发人员为中心的启发式适应平台，适用于间歇供电计算
DOI：
10.1145/3478077
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Hester, Josiah
中科院分区：
文献类型：
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作者：
Bakar, Abu;Ross, Alexander G.;Yildirim, Kasim Sinan;Hester, Josiah
无电池传感设备从周围环境中收集能量以执行传感、计算和通信。这使得以前不可能的物联网应用成为可能。这些设备面临的核心挑战是在能源可用性不稳定、随机或不规则的情况下保持有用性；这会导致执行不一致、服务丢失和电源故障。调整执行（降级或升级）似乎有望成为避免电源故障、按时完成或提高吞吐量的一种方法。然而，由于资源有限和本地信息有限，决定何时是最佳适应时间以及如何准确地适应执行是一个挑战。在本文中，我们系统地探索了能量感知适应的基本机制，并提出启发式适应作为调节任务性能的方法，以根据应用实现更高的传感器覆盖范围、完成率或吞吐量。我们为体现这一概念的间歇供电传感器构建了一个基于任务的自适应运行时系统。我们通过面向用户的模拟器来补充该运行时，使程序员能够概念化他们在选择要适应的任务以及相对于现实世界能量收集环境痕迹的适应方式时所做的权衡。虽然我们的目标是无电池、间歇供电的传感器，但我们看到了所有能量收集设备的普遍应用。我们探索了各种能量收集模式和不同应用的启发式适应：机器学习、活动识别和温室监测，并发现我们的 ML 应用程序的自适应版本的分类能力提高了 46%，而准确度仅下降了 5%；活动识别应用程序仅通过名义下采样即可捕获多 76% 的分类；并发现在所有情况下，与非自适应相比，启发式自适应会带来更高的吞吐量。
Battery-free sensing devices harvest energy from their surrounding environment to perform sensing, computation, and communication. This enables previously impossible applications in the Internet-of-Things. A core challenge for these devices is maintaining usefulness despite erratic, random or irregular energy availability; which causes inconsistent execution, loss of service and power failures. Adapting execution (degrading or upgrading) seems promising as a way to stave off power failures, meet deadlines, or increase throughput. However, because of constrained resources and limited local information, it is a challenge to decide when would be the best time to adapt, and how exactly to adapt execution. In this paper, we systematically explore the fundamental mechanisms of energy-aware adaptation, and propose heuristic adaptation as a method for modulating the performance of tasks to enable higher sensor coverage, completion rates, or throughput, depending on the application. We build a task based adaptive runtime system for intermittently powered sensors embodying this concept. We complement this runtime with a user facing simulator that enables programmers to conceptualize the tradeoffs they make when choosing what tasks to adapt, and how, relative to real world energy harvesting environment traces. While we target battery-free, intermittently powered sensors, we see general application to all energy harvesting devices. We explore heuristic adaptation with varied energy harvesting modalities and diverse applications: machine learning, activity recognition, and greenhouse monitoring, and find that the adaptive version of our ML app performs up to 46% more classifications with only a 5% drop in accuracy; the activity recognition app captures 76% more classifications with only nominal down-sampling; and find that heuristic adaptation leads to higher throughput versus non-adaptive in all cases.