MDSC: modelling distributed stream processing across the edge-to-cloud continuum

Daniel Balouek-Thomert, Pedro Silva, Kevin Fauvel, Alexandru Costan, Gabriel Antoniu, M. Parashar
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引用次数: 4

Abstract

The growth of the Internet of Things is resulting in an explosion of data volumes at the Edge of the Internet. To reduce costs incurred due to data movement and centralized cloud-based processing, it is becoming increasingly important to process and analyze such data closer to the data sources. Exploiting Edge computing capabilities for stream-based processing is however challenging. It requires addressing the complex characteristics and constraints imposed by all the resources along the data path, as well as the large set of heterogeneous data processing and management frameworks. Consequently, the community needs tools that can facilitate the modeling of this complexity and can integrate the various components involved. In this work, we introduce MDSC, a hierarchical approach for modeling distributed stream-based applications on Edge-to-Cloud continuum infrastructures. We demonstrate how MDSC can be applied to a concrete real-life ML-based application - early earthquake warning - to help answer questions such as: when is it worth decentralizing the classification load from the Cloud to the Edge and how?
MDSC:跨边缘到云连续体的分布式流处理建模
物联网的发展导致了互联网边缘数据量的爆炸式增长。为了减少由于数据移动和基于云的集中式处理而产生的成本,在离数据源更近的地方处理和分析这些数据变得越来越重要。然而,利用边缘计算能力进行基于流的处理是具有挑战性的。它需要处理数据路径上所有资源所施加的复杂特征和约束,以及大量异构数据处理和管理框架。因此,社区需要能够促进这种复杂性的建模并能够集成所涉及的各种组件的工具。在这项工作中,我们介绍了MDSC,这是一种分层方法,用于在边缘到云连续体基础设施上建模基于分布式流的应用程序。我们演示了MDSC如何应用于一个具体的现实生活中基于机器学习的应用程序——早期地震预警——以帮助回答以下问题:何时值得将分类负载从云端分散到边缘,以及如何分散?
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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