基于加速感知的边缘计算环境下深度神经网络任务Kubernetes调度程序

Jung-Gi Park, Un-Sook Choi, Seungwoo Kum, Jaewon Moon, Kyungyong Lee
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引用次数: 4

摘要

由于边缘计算在各种应用场景中的广泛采用以及为边缘环境明确开发的专用硬件,边缘设备的计算能力正在扩展。作为一个容器编排平台,Kubernetes被广泛用于有效地维护边缘计算资源,但它的调度能力有限。我们提出了一个加速器信息提取模块的设计和实现,通过提供丰富的硬件信息来提高标准Kubernetes实现的调度能力。此外,通过考虑详细的工作负载特征和附加的专用加速器硬件信息,我们提出了Kubernetes调度器的合理改进。
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Accelerator-Aware Kubernetes Scheduler for DNN Tasks on Edge Computing Environment
The compute capability of edge devices is expanding owing to the wide adoption of edge computing for various application scenarios and specialized hardware explicitly developed for an edge environ-ment. A container orchestration platform, Kubernetes is widely used to maintain edge computing resources efficiently, but it suf-fers from a limited scheduling capacity. We present a design and implementation of an accelerator information extraction module to improve the scheduling capability of a standard Kubernetes imple-mentation by providing rich hardware information. Furthermore, we present a plausible advancement of the Kubernetes scheduler by considering detailed workload characteristics and attached spe-cialized accelerator hardware information.
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