2026-09-25T00:34:19Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/124427 2024-09-16 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Kindratenko, Volodymyr 2024-05 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms The student, Zhongbo Zhu, accepted the attached license on 2024-04-28 at 11:15. The student, Zhongbo Zhu, submitted this Thesis for approval on 2024-04-28 at 11:44. This Thesis was approved for publication on 2024-04-30 at 16:49. DSpace SAF Submission Ingestion Package generated from Vireo submission #20679 on 2024-09-16 at 00:37:20 Enhancing hybrid cloud ray clusters: automated data management and innovative networking for efficient HPC cloud bursting Zhu, Zhongbo 2024-04-30 Distributed Systems Cloud Computing High Performance Computing Distributed Machine Learning The increasing reliance on hybrid systems that combine High-Performance Computing (HPC) and Cloud computing reflects their ability to manage workload surges and reduce users' queuing time. This thesis presents the development of an innovative HPC-Cloud bursting system, which leverages the Ray open-source distributed framework. Our system features an advanced automated data management mechanism and a unique dynamic label-based scheduling algorithm that intelligently manages data dependencies and minimizes data transmission overheads. My personal contributions to this project were critical in several areas: I designed and implemented network solutions and infrastructure as code, significantly simplifying the deployment and operational management of the hybrid system while also reducing costs. Additionally, I played an important role in optimizing the system's scheduler to improve stability and contributed directly to a performance boost. Our benchmarks, which focus on machine learning model training and image processing tasks, demonstrate our system's enhanced efficiency, achieving up to 20\% improvement when compared to traditional methods. Text eng https://hdl.handle.net/2142/124427 Copyright 2024 Zhongbo Zhu M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng