Abstract: Backfilling is a widely used scheduling technique in High-Performance Computing (HPC) systems to improve resource utilization. However, traditional approaches like EASY Backfill were devised for mono-core homogeneous environments, without considering the implications of multi-core architectures or the individual characteristics of nodes in heterogeneous clusters. This article proposes two refinements of EASY called Heterogeneous Backfill (HB) and Heterogeneous Reordering Backfill (HRB). These algorithms adapt the backfilling strategy to heterogeneous multi-core environments by incorporating node properties into the scheduling process. The HB algorithm sorts nodes based on a given criterion, such as power consumption or performance, to improve resource allocation. The HRB algorithm extends this approach by incorporating job reordering criteria, allowing for more efficient backfilling decisions. An evaluation of these algorithms shows that they can significantly reduce energy consumption and improve scheduling efficiency in heterogeneous clusters. The results demonstrate that the proposed algorithms outperform traditional backfilling methods, such as EASY Backfill, in terms of energy consumption, waiting time or makespan. By embracing the heterogeneity of modern HPC systems, these algorithms enable more efficient resource utilization and contribute to the overall performance of large-scale computing environments.

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