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Big Data Workflows: Locality-Aware Orchestration Using Software Containers.

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  • 1Department of Informatics, University of Oslo, 0373 Oslo, Norway.

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Summary
This summary is machine-generated.

This study introduces a new container-centric big data workflow orchestration for edge computing, prioritizing data locality. The novel solution significantly improves execution speed for processing small, frequent events compared to existing methods.

Keywords:
big data workflowsdata localityorchestrationsoftware containers

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Edge Computing

Background:

  • Edge computing shifts data processing from centralized to distributed infrastructures.
  • Data locality is crucial for performance in edge environments, but current solutions are inadequate.
  • Existing big data solutions struggle with small, frequent events typical of edge computing.

Purpose of the Study:

  • To propose a novel architecture for software container-centric big data workflow orchestration.
  • To prioritize data locality in edge computing environments.
  • To improve the efficiency of processing small and frequent events at the edge.

Main Methods:

  • Developed a novel architecture for container-centric big data workflow orchestration.
  • Leveraged long-lived containers for executing workflow steps.
  • Incorporated data locality information into the orchestration process.
  • Designed containerized interactions with diverse data sources.

Main Results:

  • Demonstrated significant performance improvements in execution speed compared to Argo workflows.
  • Validated the effectiveness of the proposed solution through experiments with varying configurations.
  • Showcased enhanced efficiency in processing small and frequent edge data events.

Conclusions:

  • The proposed container-centric architecture effectively addresses data locality challenges in edge computing.
  • The solution offers a substantial performance advantage for edge big data processing.
  • This approach is well-suited for handling the unique demands of edge environments.