2026-09-20T17:23:31Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/124598 2026-01-14 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Kindratenko, Volodymyr 2024-05 application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01 The student, Huili Tao, accepted the attached license on 2024-04-30 at 11:51. The student, Huili Tao, submitted this Thesis for approval on 2024-04-30 at 11:59. This Thesis was approved for publication on 2024-05-01 at 11:51. DSpace SAF Submission Ingestion Package generated from Vireo submission #20712 on 2024-09-16 at 00:44:53 Optimizing data movement in cloud-bursting HPC environments through dynamic labeling and prefetching strategies Tao, Huili 2024-05-01 Hpc Cloud Bursting Data Movement Hybrid High-Performance Computing-Cloud systems are gaining popularity among researchers for their ability to handle sudden demand spikes, resulting in accelerated turnaround times for High-Performance Computing (HPC) tasks. However, deploying workloads on such systems presents challenges, particularly in data migration across HPC clusters and the Cloud, and the lack of support in existing schedulers for hybrid environments. To address these issues, we present an HPC-Cloud bursting system leveraging Ray, an open-source distributed framework. Our system seamlessly integrates automated data management with data prefetching and learning-based scheduling at the function level. In this project, my primary focus was on implementing dynamic labeling within the Ray framework, enabling adjustments and modifications to node labels during the runtime. This dynamic labeling is then seamlessly integrated with the workload scheduler to facilitate strategic data prefetching to the most suitable nodes. Additionally, I played a pivotal role in enhancing the compatibility of our system with Cloud Storage Service, thereby expanding its versatility and usability. We assess the effectiveness of our framework by employing two prevalent workloads: machine learning model training and image processing. Our findings reveal that our system consistently yields advantages across diverse data locations and network speeds when compared to the manual data fetching baseline for both workloads. Text eng https://hdl.handle.net/2142/124598 Copyright 2024 Huili Tao M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng