University of Illinois Urbana-Champaign

A faster reinforcement learning approach to efficient job scheduling in Apache Spark

Gertsman, Arkadiy

Content Files
GERTSMAN-THESIS-2023.pdf

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https://hdl.handle.net/2142/121563

Description

Title
A faster reinforcement learning approach to efficient job scheduling in Apache Spark
Author(s)
Gertsman, Arkadiy
Issue Date
2023-07-21
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)
  • Reinforcement Learning
  • Graph Neural Networks
  • Job Scheduling
  • Apache Spark
Abstract
Job scheduling problems have been widely studied in theoretical computer science and operations research, and are commonly encountered in applied settings such as computer systems, manufacturing, and construction. There are many variants of job scheduling, but they share a common goal: designat- ing jobs to run on a set of parallel machines at different times, such that the machines are efficiently utilized. This thesis focuses on job scheduling in the context of Apache SparkTM, a popular data analytics engine that harnesses the power of distributed computing. Job scheduling is central to Spark, as each Spark application needs a scheduler to orchestrate its job submissions. The basic scheduling rules provided by Spark work well on lighter workloads, but sophisticated scheduling algorithms can greatly increase cluster efficiency when workloads are heavier. Previous work has introduced such algorithms, some hand-tuned and others learned. This thesis thoroughly documents Dec- ima, the state-of-the-art, reinforcement-learned Spark job scheduler, includ- ing a close look into their simulator and model architectures, and a new SMDP formulation of the problem. This thesis also proposes Decima++, an update to Decima which improves scheduling performance and reduces training time by over 11×.
Graduation Semester
2023-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/121563
Copyright and License Information
Copyright 2023 Arkadiy Gertsman

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