In this article, we address the broad issue of a responsible use of Artificial Intelligence in
Human Resources Management through the lens of a fair-by-design approach to algorithm …

Abstract High-Performance Computing (HPC) domain provided the necessary tools to
support the scientific and industrial advancements we all have seen during the last decades …

Workload prediction is critical in enabling proactive resource management of cloud
applications. Accurate workload prediction is valuable for cloud users and providers as it …

This study proposes a novel approach combining Machine Learning (ML) techniques and
Genetic Algorithms (GA) for predicting High-Performance Computing (HPC) job run times …

Dragonfly is a highly scalable, low-diameter, and cost-efficient network topology, which has
been adopted in new exascale High Performance Computing (HPC) systems. However …

Power-aware scheduling is a promising solution to the resource usage monitoring of High-
Performance Computing facility electrical power consumption. This kind of solution needs a …

Predicting resource consumption and run time of computational workloads is crucial for
efficient resource allocation, or cost and energy optimization. In this paper, we evaluate …

There are massive jobs submitted in the supercomputer, and the job management system is
typically deployed to schedule these jobs and allocate compute resources. FCFS (First …

As the amount of available data continues to grow in fields as diverse as bioinformatics,
physics, and remote sensing, the importance of scientific workflows in the design and …

In this paper, we present a novel methodology for predicting job resources (memory and
time) for submitted jobs on HPC systems. Our methodology based on historical jobs data …