Machine learning systems in constrained environments

Jeon, Beomyeol

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https://hdl.handle.net/2142/125692 Copy

Description

Title

Machine learning systems in constrained environments

Author(s)

Jeon, Beomyeol

Issue Date

2024-07-08

Director of Research (if dissertation) or Advisor (if thesis)

Gupta, Indranil

Doctoral Committee Chair(s)

Gupta, Indranil

Committee Member(s)

• Caesar, Matthew
• Park, Yongjoo
• Wang, Chen

Department of Study

Siebel Computing &DataScience

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Machine Learning Systems
• Machine Learning
• Constraints
• Placements
• Autoscaling
• Graph Neural Networks
• Serverless Computing
• Optimizations
• Algorithms

Language

eng

Abstract

Machine learning (ML) training and inference systems encounter constraints in current computation environments due to increased ML model sizes, the fast-growing popularity of ML/AI, etc. In this thesis, we show how machine learning training and inference systems can be executed successfully and efficiently in constrained computation environments, such as limited-memory GPUs, on-premises clusters, and serverless environments, by using a novel combination of algorithms, optimizations, and well-reasoned system designs. Concretely, we propose (i) a system that enables large ML model training over multiple memory-constrained GPU devices via algorithms and system designs that achieve fast placements with a quality comparable to expert-designed placements, (ii) a system that enables efficient resource sharing among ML inference jobs in fixed-size on-premises clusters by making close-to-optimal autoscaling decisions quickly via several relaxation methods in optimization and prediction, and (iii) a system that enables cost-efficient distributed GNN training on constrained serverless execution environments by auto-tuning configuration via analytic model-based offline optimization and gray-box heuristic-based online optimization.

Graduation Semester

2024-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/125692

Copyright and License Information

Copyright 2024 Beomyeol Jeon

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