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

The job scheduler plays a vital role in high-performance computing platforms. It determines
the execution order of the jobs and the allocation of resources, which in turn affect the …

First and foremost, I extend my deepest gratitude to my advisor, Prof. Dacheng **u, who has
guided me since my master's studies. Dacheng has not only been an exceptional academic …

Load balancing is a critical problem in distributed file systems. Previous works focus on
achieving data distribution across nodes at the file-level, often overlooking the potential …

Job scheduling aims to minimize the turnaround time on the submitted jobs while catering to
the resource constraints of High Performance Computing (HPC) systems. The challenge …

Spark is one of the most important big data computing engines, favored by academia and
industry for its low latency and ease of use. The explosive growth in data volumes is causing …

Predicting the runtime of tasks is of great significance as it can help users better understand
the future runtime consumption of the tasks and make decisions for their heterogeneous …

As high‐performance computing (HPC) becomes a tool used in many different workflows,
quality of service (QoS) becomes increasingly important. In many cases, this includes the …

With the increase of demand for computing resources and the struggle to provide the
necessary energy, power-aware resource management is becoming a major issue for the …

Motivated by an imperative to reduce the carbon emissions of cloud data centers, this paper
studies the online carbon-aware resource scaling problem with unknown job lengths …