Current Cloud solutions for Edge Computing are inefficient for data-centric applications, as they focus on the IaaS/PaaS level and they miss the data modeling and operations perspective. Consequently, Edge Computing opportunities are lost due to cumbersome and data assets-agnostic processes for end-to-end deployment over the Cloud-to-Edge continuum. In this paper, we introduce MEDAL, an intelligent Cloud-to-Edge Data Fabric to support Data Operations (DataOps)across the continuum and to automate management and orchestration operations over a combined view of the data and the resource layer. MEDAL facilitates building and managing data workflows on top of existing flexible and composable data services, seamlessly exploiting and federating IaaS/PaaS/SaaS resources across different Cloud and Edge environments. We describe the MEDAL Platform as a usable tool for Data Scientists and Engineers, encompassing our concept and we illustrate its application though a connected cars use case.

中文翻译：

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MEDAL：弹性云到边缘智能的AI驱动数据结构概念

当前的边缘计算云解决方案对于以数据为中心的应用程序效率低下，因为它们专注于IaaS / PaaS级别，并且错过了数据建模和操作的角度。因此，由于在云到边缘连续体上进行端到端部署的繁琐且与数据资产无关的流程，导致边缘计算机会丧失。在本文中，我们介绍了MEDAL，它是一种智能的云到边缘数据结构，可支持整个连续体中的数据操作（DataOps），并可在数据和资源层的组合视图上自动进行管理和编排操作。MEDAL有助于在现有的灵活且可组合的数据服务之上构建和管理数据工作流，从而跨不同的Cloud和Edge环境无缝地开发和联合IaaS / PaaS / SaaS资源。