Risco Sebastián, Moltó Germán, Naranjo Diana M, Blanquer Ignacio
Instituto de Instrumentación para Imagen Molecular (I3M), Centro mixto CSIC - Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, España.
J Grid Comput. 2021;19(3):30. doi: 10.1007/s10723-021-09570-2. Epub 2021 Jul 13.
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（即同时涉及本地和公共云平台来处理在边缘捕获的数据）的基于科学数据处理工作流的无服务器计算应用程序。这是通过为与零扩展方法兼容的FaaS平台进行动态资源配置来实现的，该方法可将具有不同弹性要求的动态工作负载的资源使用和成本降至最低。该平台结合了在本地云上动态部署的自动扩展Kubernetes集群的使用以及自动云突发到AWS Lambda中，以实现更高的弹性水平。一个用于智慧城市公共卫生的用例被用来评估该平台，该平台负责从捕获的视频中检测未戴口罩的人。为了在本地云中增强匿名性，面部会被模糊处理，并通过深度学习模型在AWS Lambda中对该数据驱动的容器化工作流进行检测。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合规性，并执行云突发以提高弹性水平。
