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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Distributed in-memory data management for workflow executions.

Renan Souza1,2, Vitor Silva1,3, Alexandre A B Lima1,4

  • 1COPPE/Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.

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|May 20, 2021
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Summary
This summary is machine-generated.

SchalaDB enhances parallel workflow management systems (WMS) with distributed in-memory data management, enabling efficient execution control and user steering. This approach minimizes overhead for complex scientific workflows, even with runtime data analysis.

Keywords:
Distributed databaseIn-memory databaseParallel workflow management systemsTask schedulingUser steering

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Area of Science:

  • Computer Science
  • High-Performance Computing
  • Data Management

Background:

  • Scientific experiments are modeled as workflows executed on large-scale machines using Parallel Workflow Management Systems (WMS).
  • User steering, allowing runtime workflow adaptation based on data analysis, is a crucial feature for long-running scientific experiments.
  • Managing hybrid workloads with transaction-oriented and online analytical data access presents a significant challenge for WMS.

Purpose of the Study:

  • To present SchalaDB, an architecture for efficient workflow execution control and user steering.
  • To propose a distributed data design for scalable workflow task scheduling and high availability.
  • To enable effective management of hybrid data access patterns within WMS.

Main Methods:

  • Developed SchalaDB, an architecture based on distributed in-memory data management principles.
  • Designed a distributed data model for scalable task scheduling and high availability using a parallel and distributed in-memory DBMS.
  • Implemented d-Chiron, a WMS based on SchalaDB principles, and evaluated it on an HPC cluster.

Main Results:

  • SchalaDB demonstrates negligible overhead for user steering data analyses on large-scale, shared-data workloads.
  • The proposed architecture supports efficient workflow execution control and high availability.
  • Experimental evaluation on up to 960 cores confirms the system's scalability and performance.

Conclusions:

  • SchalaDB provides an effective solution for managing complex scientific workflows with user steering capabilities.
  • A parallel and distributed data-oriented approach is recommended for WMS development, encompassing scheduling, monitoring, and user steering.
  • The findings encourage wider adoption of in-memory data management techniques in workflow execution.