Exploratory research of Lagrangian relaxation for cloud workflow scheduling
Kang, Yoonhwan
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https://hdl.handle.net/2142/125638
Description
Title
Exploratory research of Lagrangian relaxation for cloud workflow scheduling
Author(s)
Kang, Yoonhwan
Issue Date
2024-07-18
Director of Research (if dissertation) or Advisor (if thesis)
Nagi, Rakesh
Department of Study
Industrial&Enterprise Sys Eng
Discipline
Industrial Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Cloud Workflow Scheduling
Lagrangian Relaxation (lr)
Gap Closure Theme
Subgradient Method.
Language
eng
Abstract
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.
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