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A survey of pipelined workflow scheduling: Models and algorithms

Published: 30 August 2013 Publication History

Abstract

A large class of applications need to execute the same workflow on different datasets of identical size. Efficient execution of such applications necessitates intelligent distribution of the application components and tasks on a parallel machine, and the execution can be orchestrated by utilizing task, data, pipelined, and/or replicated parallelism. The scheduling problem that encompasses all of these techniques is called pipelined workflow scheduling, and it has been widely studied in the last decade. Multiple models and algorithms have flourished to tackle various programming paradigms, constraints, machine behaviors, or optimization goals. This article surveys the field by summing up and structuring known results and approaches.

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  • (2023)Distributed Edge Machine Learning Pipeline Scheduling with Reverse Auctions2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC)10.1109/FMEC59375.2023.10306169(196-203)Online publication date: 18-Sep-2023
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Published In

cover image ACM Computing Surveys
ACM Computing Surveys  Volume 45, Issue 4
August 2013
490 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/2501654
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 30 August 2013
Accepted: 01 August 2012
Revised: 01 September 2011
Received: 01 June 2010
Published in CSUR Volume 45, Issue 4

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Author Tags

  1. Workflow programming
  2. algorithms
  3. distributed systems
  4. filter-stream programming
  5. latency
  6. models
  7. parallel systems
  8. pipeline
  9. scheduling
  10. throughput

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  • Refereed

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  • (2023)Distributed Edge Machine Learning Pipeline Scheduling with Reverse Auctions2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC)10.1109/FMEC59375.2023.10306169(196-203)Online publication date: 18-Sep-2023
  • (2023)Concurrent Cyclic ProcessesDeclarative Models of Concurrent Cyclic Processes10.1007/978-3-031-40552-5_1(1-36)Online publication date: 31-Oct-2023
  • (2022)Design and Development of Maritime Data Security Management PlatformApplied Sciences10.3390/app1202080012:2(800)Online publication date: 13-Jan-2022
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  • (2021)A Comparative Study and Algorithmic Analysis of Workflow Decomposition in Distributed SystemsResearch Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing10.4018/978-1-7998-5339-8.ch112(2282-2315)Online publication date: 2021
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  • (2020)Using Reduced Graphs for Efficient HLS Scheduling2020 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS45731.2020.9181274(1-5)Online publication date: Oct-2020
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