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Component Description
The project aims to evaluate the seamless integration and performance of the Kueue batch scheduling system within the REANA platform, a robust open-source solution designed for reproducible and declarative data analysis in containerized computing clouds. As the REANA platform facilitates streamlined workflows for researchers in the field of High-Energy Physics (HEP), the project focus relies on testing the viability of Kueue within the context of runtime user jobs, laying the groundwork for future developments that may introduce FAIR share capabilities within the REANA ecosystem.
The ongoing replication crisis, characterized by the difficulty or impossibility of reproducing scientific study results, has prompted a methodological crisis within the scientific community. In response to this challenge, REANA provides researchers with a platform to conduct High-Energy Physics (HEP) data analyses in a transparent, reproducible, and scalable manner. With its ability to support various backends, REANA enables researchers to leverage specific resources customized to their computational requirements, facilitating the execution of containerized workflow analyses. However, a pivotal challenge remains in dynamically scheduling workflows across diverse types of resources, forming the crux of the integration endeavor with Kueue.
The integration of Kueue into REANA is justified by the prospect of advancing the platform's capabilities, making it more adaptable to evolving user needs in the field of HEP data analyses. By replacing Kubernetes API calls with Kueue API calls, the integration of Kueue serves a multifaceted purpose. Beyond introducing FAIR Sharing capabilities, Kueue adds a crucial layer of flexibility by enabling dynamic container execution across different types of hardware. This capability is paramount in the current scientific landscape, where research projects often require diverse computational resources.
In conclusion, the integration of Kueue into the REANA platform marks a significant step towards fortifying its capabilities and adapting to emerging requirements in the realm of reproducible data analyses. The problem approach focuses on the practical implementation of Kueue, transitioning from Kubernetes API calls and laying the groundwork for future developments, especially the integration of FAIR share capabilities. This initiative aligns with the broader goals of REANA, emphasizing reproducibility and declarative workflows, and serves as a testament to the platform's commitment to continuous improvement and adaptability in the rapidly evolving landscape of scientific computing. The successful integration of Kueue is poised to contribute to a more versatile and user-friendly REANA platform for researchers in the field.