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Serverless Workflows for Containerised Applications in the Cloud Continuum
Sebastián Risco1, Germán Moltó1, Diana M Naranjo1
1Instituto de Instrumentación para Imagen Molecular (I3M), Centro mixto CSIC - Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, España.
Journal of Grid Computing
|July 19, 2021
Summary
This study presents an open-source platform for serverless scientific computing across hybrid clouds. It enables dynamic resource scaling and cloud bursting for efficient data processing and cost savings.
Area of Science:
- Computer Science
- Cloud Computing
- Scientific Computing
Background:
- Serverless computing offers cost-efficiency for dynamic workloads.
- Processing scientific data often requires hybrid cloud environments for flexibility and compliance.
- Edge computing generates data that needs scalable processing solutions.
Purpose of the Study:
- To introduce an open-source platform for serverless scientific data-processing workflows across the cloud continuum.
- To enable dynamic resource provisioning and scale-to-zero approaches for FaaS platforms.
- To demonstrate efficient hybrid cloud continuum utilization for data processing.
Main Methods:
- Developed a platform combining auto-scaled Kubernetes clusters on-premises with AWS Lambda cloud bursting.
- Implemented dynamic resource provisioning for Function-as-a-Service (FaaS) platforms.
- Utilized a public health use case in smart cities for platform assessment.
Main Results:
- The platform efficiently handles dynamic workloads with varying elasticity requirements.
- Hybrid workflows demonstrated effective local data processing for regulatory compliance.
- Cloud bursting achieved enhanced elasticity for data-intensive tasks.
Conclusions:
- Open-source platform supports serverless computing across on-premises and public clouds.
- Dynamic resource provisioning and cloud bursting enhance elasticity and reduce costs.
- Hybrid cloud continuum workflows are viable for compliant and elastic scientific data processing.
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