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  • © 2019

Fair Scheduling in High Performance Computing Environments

  • With the prevalence of shared computing environments (cloud for example), how a shared resource (such as a computer/CPU/Core) is distributed and time-shared across many users will be critically important.

  • Current methods and algorithms used to divide resources will simply not work in large shared computing environments. For a large organization such as a bank, or pharma, the method introduced in this book will allow them to create internal shared services hat will scale to the enterprise.

  • This book applies behavioral economics to computing – which opens the door for many applications across the technology sector to do the same.

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xi
  2. Introduction

    • Art Sedighi, Milton Smith
    Pages 1-5
  3. Financial Market Risk

    • Art Sedighi, Milton Smith
    Pages 7-15
  4. Scheduling in High Performance Computing

    • Art Sedighi, Milton Smith
    Pages 17-22
  5. Fairshare Scheduling

    • Art Sedighi, Milton Smith
    Pages 23-28
  6. Multi-Criteria Scheduling: A Mathematical Model

    • Art Sedighi, Milton Smith
    Pages 29-37
  7. Simulation and Methodology

    • Art Sedighi, Milton Smith
    Pages 39-46
  8. DSIM

    • Art Sedighi, Milton Smith
    Pages 47-50
  9. Simulation Scenarios

    • Art Sedighi, Milton Smith
    Pages 51-62
  10. Overview of Results

    • Art Sedighi, Milton Smith
    Pages 63-70
  11. Class A Results and Analysis

    • Art Sedighi, Milton Smith
    Pages 71-84
  12. Class B Results and Analysis

    • Art Sedighi, Milton Smith
    Pages 85-99
  13. Class C Results and Analysis

    • Art Sedighi, Milton Smith
    Pages 101-109
  14. Class D Results and Simulations

    • Art Sedighi, Milton Smith
    Pages 111-122
  15. Conclusion

    • Art Sedighi, Milton Smith
    Pages 123-124
  16. Back Matter

    Pages 125-132

About this book

This book introduces a new scheduler to fairly and efficiently distribute system resources to many users of varying usage patterns compete for them in large shared computing environments.  The Rawlsian Fair scheduler developed for this effort is shown to boost performance while reducing delay in high performance computing workloads of certain types including the following four types examined in this book:

i.        Class A – similar but complementary workloads

ii.      Class B – similar but steady vs intermittent workloads

iii.    Class C – Large vs small workloads

iv.    Class D – Large vs noise-like workloads

This new scheduler achieves short-term fairness for small timescale demanding rapid response to varying workloads and usage profiles.  Rawlsian Fair scheduler is shown to consistently benefit workload Classes C and D while it only benefits Classes A and B workloads where they become disproportionate as the number of users increases.

A simulation framework, dSim, simulates the new Rawlsian Fair scheduling mechanism. The dSim helps achieve instantaneous fairness in High Performance Computing environments, effective utilization of computing resources, and user satisfaction through the Rawlsian Fair scheduler.

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Authors and Affiliations

  • Industrial, Manufacturing & Systems Engineering, Texas Tech University, Lubbock, USA

    Art Sedighi, Milton Smith

About the authors

  

Bibliographic Information

Buy it now

Buying options

eBook USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access