论文标题
边缘到云连续性的分布式智能：系统文献综述
Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review
论文作者
论文摘要
通过越来越多的应用程序生成的数据量的爆炸正在强烈影响分布式数字基础架构用于数据分析和机器学习（ML）的演变。尽管数据分析过去主要是在云基础架构上进行的，但物联网基础架构的快速发展以及对低延迟的要求，安全处理促使了Edge Analytics的发展。如今，为了平衡各种权衡，基于ML的分析倾向于越来越利用互连的生态系统，该系统允许在混合基础架构上执行复杂的应用程序，在该混合基础架构上，在该混合基础架构上，IoT Edge设备与云/HPC系统互连在所谓的计算连续性，数字连续性，数字连续性或跨性别的iS the IS基于学习的分析中，这些分析构成了更加困难的分析。在边缘到云连续体中基于学习的工作流的大规模和优化部署需要对代表性测试床上的应用执行进行广泛且可重现的实验分析。这是有助于了解组合各种学习范式和支持性框架所产生的性能权衡的必要条件。彻底的实验分析需要评估多种因素的影响，例如：模型的准确性，训练时间，网络间接费用，能源消耗，处理潜伏期等。此评论旨在为当今可用的机器学习和数据分析提供全面的目的。它描述了主要的学习范式，从而使基于学习的分析在边缘到云连续性方面。当今的主要模拟，仿真，部署系统和测试台，用于当今可用的边缘连续体的实验研究。此外，我们分析了所选系统如何为实验可重复性提供支持。我们通过详细讨论了有关开放研究挑战的详细讨论和该领域中未来的方向，例如：整体对绩效的理解；应用程序的性能优化；在高度异构基础架构上有效地部署人工智能（AI）工作流程；以及对计算连续性实验的可再现分析。
The explosion of data volumes generated by an increasing number of applications is strongly impacting the evolution of distributed digital infrastructures for data analytics and machine learning (ML). While data analytics used to be mainly performed on cloud infrastructures, the rapid development of IoT infrastructures and the requirements for low-latency, secure processing has motivated the development of edge analytics. Today, to balance various trade-offs, ML-based analytics tends to increasingly leverage an interconnected ecosystem that allows complex applications to be executed on hybrid infrastructures where IoT Edge devices are interconnected to Cloud/HPC systems in what is called the Computing Continuum, the Digital Continuum, or the Transcontinuum.Enabling learning-based analytics on such complex infrastructures is challenging. The large scale and optimized deployment of learning-based workflows across the Edge-to-Cloud Continuum requires extensive and reproducible experimental analysis of the application execution on representative testbeds. This is necessary to help understand the performance trade-offs that result from combining a variety of learning paradigms and supportive frameworks. A thorough experimental analysis requires the assessment of the impact of multiple factors, such as: model accuracy, training time, network overhead, energy consumption, processing latency, among others.This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. It describes the main learning paradigms enabling learning-based analytics on the Edge-to-Cloud Continuum. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed. Furthermore, we analyze how the selected systems provide support for experiment reproducibility. We conclude our review with a detailed discussion of relevant open research challenges and of future directions in this domain such as: holistic understanding of performance; performance optimization of applications;efficient deployment of Artificial Intelligence (AI) workflows on highly heterogeneous infrastructures; and reproducible analysis of experiments on the Computing Continuum.
