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Visualizing Visual Adaptation
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Adaptive On-the-Fly Changes in Distributed Processing Pipelines.

Toon Albers1, Elena Lazovik1, Mostafa Hadadian Nejad Yousefi2

  • 1Monitoring & Control Services Department, TNO, Groningen, Netherlands.

Frontiers in Big Data
|December 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces automated reconfiguration for distributed data processing pipelines, enabling on-the-fly updates without system restarts. This innovation simplifies complex big data analytics for users with less development experience.

Keywords:
adaptive dynamic systemsbig data applicationsdistributed computingdynamic software updatingindustrial data managementon-the-fly updates

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Area of Science:

  • Computer Science
  • Data Engineering
  • Software Systems

Background:

  • Distributed data processing systems are standard for big data analytics.
  • Processing pipelines execute operations in consecutive steps, typically fixed at design time.
  • Modifying pipelines requires application restarts, which is often unacceptable due to downtime or lost progress.

Purpose of the Study:

  • To enable on-the-fly updating of individual analysis steps in distributed processing pipelines.
  • To introduce fully automated reconfiguration of processing steps within a running pipeline using an automated planner.
  • To simplify pipeline reconfiguration for users with less development experience.

Main Methods:

  • Introduced variation points for on-the-fly updating of pipeline steps.
  • Developed an automated planner for pipeline reconfiguration.
  • Enabled pipeline modeling through constraints for type compatibility and functionality verification.
  • Automatically generated, validated, and integrated new pipeline configurations into running systems.

Main Results:

  • Demonstrated a proof-of-concept implementation of the automated reconfiguration system.
  • Verified the system's ability to ensure type compatibility and achieve specified pipeline functionality.
  • Showed promising results, especially when reconfiguration is performed frequently.

Conclusions:

  • Automated reconfiguration of distributed processing pipelines is feasible and beneficial.
  • The system simplifies complex pipeline modifications, making them accessible to a wider range of users.
  • The approach supports dynamic adaptation of big data analytics pipelines without downtime.