In today's computing environment, where Artificial Intelligence (AI) and data processing are moving toward the Internet of Things (IoT) and the Edge computing paradigm, benchmarking resource-constrained devices is a critical task to evaluate their suitability and performance. The literature has extensively explored the performance of IoT devices when running high-level benchmarks specialized in particular application scenarios, such as AI or medical applications. However, lower-level benchmarking applications and datasets that analyze the hardware components of each device are needed. This low-level device understanding enables new AI solutions for network, system and service management based on device performance, such as individual device identification, so it is an area worth exploring more in detail. In this paper, we present LwHBench, a low-level hardware benchmarking application for Single-Board Computers that measures the performance of CPU, GPU, Memory and Storage taking into account the component constraints in these types of devices. LwHBench has been implemented for Raspberry Pi devices and run for 100 days on a set of 45 devices to generate an extensive dataset that allows the usage of AI techniques in different application scenarios. Finally, to demonstrate the inter-scenario capability of the created dataset, a series of AI-enabled use cases about device identification and context impact on performance are presented as examples and exploration of the published data.
翻译:在当今的计算环境中,人工智能(AI)和数据处理正在转向物联网(IoT)和Edge计算范式,对资源限制装置进行基准化是评估其是否合适和性能的关键任务。文献广泛探讨了在运行高水平基准时,IoT装置的性能,这些高水平基准是专门应用情景,如AI或医疗应用。然而,低级别基准应用和数据集分析每个装置的硬件组件。这种低级别的设备理解使得基于设备性能(如个人设备识别)的网络、系统和服务管理能够采用新的AI解决方案,因此这是一个值得更详细探讨的领域。在本文件中,我们介绍了单层计算机的低水平硬件基准化应用程序LwHBench,这是一个用于测量CPU、GPU、记忆和存储性能的低级别的硬件基准化应用程序,其中考虑到这类装置的组件限制。LwHBench用于Rasperry Pi装置,并在一套45个装置上运行100天,以生成广泛的数据集集,以便能够在不同应用情景下使用AI技术。最后,展示了已出版的AIS系列数据识别能力。