Abstract
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.
Change history
19 October 2021
Springer Nature’s version of this paper was updated to add the funding information: Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
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Acknowledgements
The authors would like to thank the European Union for the project “Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum” (AI-SPRINT), with code 101016577, funded under the H2020 Framework Programme and also the regional government of the Comunitat Valenciana (Spain) for the project IDIFEDER/2018/032 (High-Performance Algorithms for the Modeling, Simulation and early Detection of diseases in Personalized Medicine), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014–2020.
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Risco, S., Moltó, G., Naranjo, D.M. et al. Serverless Workflows for Containerised Applications in the Cloud Continuum. J Grid Computing 19, 30 (2021). https://doi.org/10.1007/s10723-021-09570-2
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DOI: https://doi.org/10.1007/s10723-021-09570-2
Keywords
- Cloud computing
- Serverless computing
- Workflow
- Containers