2026-09-21T01:23:18Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/125638 2025-02-06 col_2142_5131 col_2142_16359 com_2142_5130 com_2142_16358 com_2142_234

application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms The student, Yoonhwan Kang, accepted the attached license on 2024-07-15 at 15:16. The student, Yoonhwan Kang, submitted this Thesis for approval on 2024-07-15 at 15:23. This Thesis was approved for publication on 2024-07-18 at 08:15. DSpace SAF Submission Ingestion Package generated from Vireo submission #21120 on 2025-02-04 at 21:05:23 Demand for efficient cloud workflow scheduling solutions is increasing, particularly for managing large-scale datasets. The cloud workflow scheduling problem formulated as a mixed integer linear programming (MILP) problem, requires significant computational time as the dataset scale increases. Consequently, various approaches have been studied to relax the problem into a more solvable form. This thesis presents an exploratory study on applying Lagrangian Relaxation to the cloud workflow scheduling problem. We propose a MILP formulation incorporating moving costs to reflect real-world scenarios better. By applying the Lagrangian relaxation approach and additional methods, we can obtain tight near-optimal solutions quickly. These solutions can serve as lower bounds for the MILP, enabling a reduction in computational time. 2024-08 Thesis https://hdl.handle.net/2142/125638 Copyright 2024 Yoonhwan Kang Exploratory research of Lagrangian relaxation for cloud workflow scheduling Kang, Yoonhwan 2024-07-18 Nagi, Rakesh Cloud Workflow Scheduling Lagrangian Relaxation (lr) Gap Closure Theme Subgradient Method. eng Industrial&Enterprise Sys Eng Industrial Engineering University of Illinois at Urbana-Champaign M.S. Thesis