This paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge). This is achieved via dynamic resource provisioning for FaaS platforms compatible with scale-to-zero approaches that minimise resource usage and cost for dynamic workloads with different elasticity requirements. The platform combines the usage of dynamically deployed auto-scaled Kubernetes clusters on on-premises Clouds and automated Cloud bursting into AWS Lambda to achieve higher levels of elasticity. A use case in public health for smart cities is used to assess the platform, in charge of detecting people not wearing face masks from captured videos. Faces are blurred for enhanced anonymity in the on-premises Cloud and detection via Deep Learning models is performed in AWS Lambda for this data-driven containerised workflow. The results indicate that hybrid workflows across the Cloud continuum can efficiently perform local data processing for enhanced regulations compliance and perform Cloud bursting for increased levels of elasticity.

本文介绍了一个开源平台，以支持跨云连续体的基于科学数据处理工作流的应用程序的无服务器计算（即同时涉及本地和公共云平台以处理在边缘捕获的数据）。这是通过为 FaaS 平台提供动态资源来实现的，该平台与归零方法兼容，可最大限度地减少具有不同弹性要求的动态工作负载的资源使用和成本。该平台结合了在本地云上动态部署的自动扩展 Kubernetes 集群和自动云突入 AWS Lambda 以实现更高级别的弹性。智能城市公共卫生用例用于评估该平台，负责从捕获的视频中检测未戴口罩的人。面部被模糊以增强本地云中的匿名性，并且通过深度学习模型在 AWS Lambda 中执行此数据驱动的容器化工作流程的检测。结果表明，跨云连续体的混合工作流可以有效地执行本地数据处理以增强法规遵从性，并执行云爆发以提高弹性水平。