A Dynamic, Hierarchical Resource Model for Converged Computing
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Extreme dynamic heterogeneity in high performance computing systems and the convergence of traditional HPC with new simulation, analysis, and data science approaches impose increasingly more complex requirements on resource and job management software (RJMS). However, there is a paucity of RJMS techniques that can solve key technical challenges associated with those new requirements, particularly when they are coupled. In this paper, we propose a novel dynamic and multi-level resource model approach to address three key well-known challenges individually and in combination: i.e., 1) RJMS dynamism to facilitate job and workflow adaptability, 2) integration of specialized external resources (e.g. user-centric cloud bursting), and 3) scheduling cloud orchestration framework tasks. The core idea is to combine a dynamic directed graph resource model with fully hierarchical scheduling to provide a unified solution to all three key challenges. Our empirical and analytical evaluations of the solution using our prototype extension to Fluxion, a production hierarchical graph-based scheduler, suggest that our unified solution can significantly improve flexibility, performance and scalability across all three problems in comparison to limited traditional approaches.
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