{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124427"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124427","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Enhancing hybrid cloud ray clusters: automated data management and innovative networking for efficient HPC cloud bursting","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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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","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Enhancing hybrid cloud ray clusters: automated data management and innovative networking for efficient HPC cloud bursting"]}]}],"canonical_facts":{"dc:contributor":["Kindratenko, Volodymyr"],"dc:creator":["Zhu, Zhongbo"],"dc:date":["2024-05","2024-04-30"],"dc:description":["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","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124427"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Zhongbo Zhu"],"dc:subject":["Distributed Systems","Cloud Computing","High Performance Computing","Distributed Machine Learning"],"dc:title":["Enhancing hybrid cloud ray clusters: automated data management and innovative networking for efficient HPC cloud bursting"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}