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  1. SAFe Program
  2. SP-4870

Science Enabling v0.1 - Roadmap

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    • Science Enabling v0.1 - Roadmap
    • SRCnet
    • 0

    Description

      Key Features and Objectives

      Storage Benchmarking:

      • Run tests on at least one SRC (Science Regional Center) to gather metrics and statistics for different storage types. This builds on prior benchmark development and documentation.

      Data Processing Pipeline Development:

      • Collaborate with CanSRC (Red team) to establish a data processing pipeline utilizing Rucio datalake and CADC CANFAR for distributed data transfer and processing.
      • Key Acceptance Criteria (AC):
      • Upload astronomy data products to Rucio datalake.
      • Upload custom-built images to CANFAR for batch processing.
      • Create a multi-stage data processing workflow.
      • Provide documentation for reproducibility.

      Integration of Tools and Feedback:

      • Work with OSKAR and RASCIL developers to improve API alignment, focusing on user needs for processing astronomical data.
      • Address issues related to noise configuration in simulated data to improve data processing accuracy.

      Software Distribution System:

      • Implement a software distribution system as part of the Science Enabling product, incorporating user feedback and common use cases to develop a preliminary list of containers and standard environments.

      Demonstrator Workloads:

      • Utilize UKSRC demonstrator cases with pathfinder and precursor data, allowing these to serve as test workloads for SRCNet.

      SODA Service Extensions:

      • Extend Orange’s SODA service with new operations for data cubes, ensuring client compatibility with updated functionalities.

      Visualization Integration:

      • Integrate AladinLite into the SRCNet Science Gateway for enhanced data visualization and analysis, enabling users to overlay SKA datasets on public HiPS (Hierarchical Progressive Surveys).

      Kubernetes Job Scheduling:

      • Focus on low-level job scheduling within Kubernetes, implementing prioritized queuing for interactive versus batch jobs, potentially utilizing KUEUE for efficient resource management.

      Workflow Performance Tracking:

      • Develop the PyProfQueue package to track hardware usage during workflows, validating its performance against SRCNet workloads and providing insights into resource needs.

      Documentation and Deployment Procedures:

      • Create documentation for the deployment of essential services in SRCNet v0.1, emphasizing GitOps practices and error handling.

      Simulated Data Generation:

      • Generate test data using the Karabo pipeline, seeking input on data sizes useful for initial implementation.

      End-to-End Data Lifecycle Tests:

      • Conduct tests covering data ingestion, replication, processing, and new data uploads, documenting results for future campaigns.

      Cosmic Magnetism Survey:

      • Focus on all-sky spectro-polarimetric surveys using RM synthesis with related telescopes, leveraging public data until proprietary pipelines are available.

      Software Distribution Technologies:

      • Refine recommendations for software distribution in SRCNet based on previous investigations and user expectations.

      HiPS Service Interaction:

      • Investigate integration of HiPS services through VisIVO/CARTA for hierarchical tiling of sky regions, facilitating a progressive survey view.

      CI/CD Pipeline Implementation:

      • Establish a robust CI/CD pipeline for service deployment in Kubernetes, incorporating GitOps principles for automated updates.

      3D Visualization Service Development:

      • Define capabilities for a 3D visualization service based on IVOA and X3D standards.

      Architecture Document Support:

      ••Provide architectural support in preparation for external reviews, ensuring comprehensive coverage of key areas like data discovery and access.

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                Debashis.Mitra Mitra, Debashis
                Debashis.Mitra Mitra, Debashis
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                  Updated:

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