Sparta: Heat-Budget-Based Scheduling Framework on IoT Edge Systems
Sparta: Heat-Budget-Based Scheduling Framework on IoT Edge Systems
复制标题
Sparta：物联网边缘系统上基于热预算的调度框架
DOI：
10.1007/978-3-030-96504-4_2
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Wolski, R.
中科院分区：
文献类型：
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作者：
Zhang, M.;Krintz, C.;Wolski, R.
处理基础设施和物联网设备在边缘的协同定位用于减少物联网应用的响应延迟和长途网络使用。因此，许多应用（例如农业、生态和智慧城市部署）的边缘云必须在远程、无人值守和环境恶劣的环境中运行，这带来了新的挑战。一个关键的挑战是热暴露，这会降低电子产品的性能，可靠性和寿命。对于边缘云来说，这些问题会加剧，因为它们越来越多地执行复杂的工作负载，例如机器学习，从而影响环境中设备和系统的数据驱动和控制。我们工作的目标是保护边缘云免受过热的影响。为了实现这一点，我们开发了一个热预算为基础的调度系统，称为斯巴达，它利用动态电压和频率缩放（DVFS）自适应控制CPU温度。斯巴达将机器学习应用程序、数据集和温度阈值作为输入。它根据历史数据设置CPU的初始频率，然后根据应用程序的执行配置文件和环境温度动态更新，以保护边缘设备。我们发现，对于一套机器学习应用程序和部署温度，斯巴达能够在94%的时间内将CPU温度保持在阈值以下，同时与竞争方法相比，执行时间缩短了1.04 - 1.32倍。
Co-location of processing infrastructure and IoT devices at the edge is used to reduce response latency and long-haul network use for IoT applications. As a result, edge clouds for many applications (e.g. agriculture, ecology, and smart city deployments) must operate in remote, unattended, and environmentally harsh settings, introducing new challenges. One key challenge is heat exposure, which can degrade the performance, reliability, and longevity of electronics. For edge clouds, these problems are exacerbated because they increasingly perform complex workloads, such as machine learning, to affect data-driven actuation and control of devices and systems in the environment.The goal of our work is to protect edge clouds from overheating. To enable this, we develop a heat-budget-based scheduling system, called Sparta, which leverages dynamic voltage and frequency scaling (DVFS) to adaptively control CPU temperature. Sparta takes machine learning applications, datasets, and a temperature threshold as input. It sets the initial frequency of the CPU based on historical data and then dynamically updates it, according to the applications’ execution profile and ambient temperature, to safeguard edge devices. We find that for a suite of machine learning applications and deployment temperatures, Sparta is able to maintain CPU temperature below the threshold 94% of the time while facilitating improvements in execution time by 1.04x − 1.32x over competitive approaches.