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Serverless Workflows for Containerised Applications in the Cloud Continuum.

Sebastián Risco1, Germán Moltó1, Diana M Naranjo1

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Journal of Grid Computing
|July 19, 2021
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Cloud computingContainersServerless computingWorkflow

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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.