As the next generation of diverse workloads like autonomous driving and augmented/virtual reality evolves, computation is shifting from cloud-based services to the edge, leading to the emergence of a cloud-edge compute continuum. This continuum promises a wide spectrum of deployment opportunities for workloads that can leverage the strengths of cloud (scalable infrastructure, high reliability) and edge (energy efficient, low latencies). Despite its promises, the continuum has only been studied in silos of various computing models, thus lacking strong end-to-end theoretical and engineering foundations for computing and resource management across the continuum. Consequently, developers resort to ad hoc approaches to reason about performance and resource utilization of workloads in the continuum. In this work, we conduct a first-of-its-kind systematic study of various computing models, identify salient properties, and make a case to unify them under a compute continuum reference architecture. This architecture provides an end-to-end analysis framework for developers to reason about resource management, workload distribution, and performance analysis. We demonstrate the utility of the reference architecture by analyzing two popular continuum workloads, deep learning and industrial IoT. We have developed an accompanying deployment and benchmarking framework and first-order analytical model for quantitative reasoning of continuum workloads. The framework is open-sourced and available at https://github.com/atlarge-research/continuum.
翻译:随着诸如自主驱动和增强/虚拟现实等下一代不同工作量的演化,计算工作正在从云型服务转向边缘,导致出现云型计算连续体。这一连续体将带来一系列广泛的工作量部署机会,以利用云型(可缩放的基础设施、高可靠性)和边缘(节能、低延迟)的优势。尽管其承诺,但连续体只在各种计算模型的筒仓中进行了研究,从而缺乏强大的终端到终端的理论和工程基础来进行整个连续体的计算和资源管理。因此,开发商采用临时办法来解释连续体中工作量的业绩和资源利用情况。在这项工作中,我们对各种计算模型进行首次的系统化研究,确定突出的特性,并论证将其统一到一个可调制连续参考结构之下。这一结构为开发商提供了一个端到终端的分析框架,以了解资源管理、工作量分配和绩效分析。我们通过分析两种大众连续体工作量、深层次学习和工业模型,展示了参考结构的效用。我们开发了一种对各种计算模型的首次系统化系统化的系统化系统化系统化分析框架。我们开发了一套用于进行定量/基础分析的定量分析。</s>