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010   $a3-030-14568-9
024 7 $a10.1007/978-3-030-14568-2
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035   $a(MiAaPQ)EBC5755065
035   $a(DE-He213)978-3-030-14568-2
035   $a(PPN)235671444
035   $a(EXLCZ)994100000007992483
100   $a20190417d2019      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aFair Scheduling in High Performance Computing Environments /$fby Art Sedighi, Milton Smith
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (136 pages)
311   $a3-030-14567-0 
327   $aChapter 1 Introduction 1 -- Chapter 2 Financial Market Risk 9 -- Chapter 3 Scheduling in High Performance Computing 24 -- Chapter 4 Fairshare Scheduling 33 -- Chapter 5 Multi-Criteria Scheduling: A Mathematical Model 43 -- Chapter 6 Simulation & Methodology 56 -- Chapter 7 DSIM 67 -- Chapter 8 Simulation Scenarios 73 -- Chapter 9 Overview of Results 90 -- Chapter 10 Class A Results and Analysis 101 -- Chapter 11 Class B Results and Analysis 118 -- Chapter 12 Class C Results and Analysis 139 -- Chapter 13 Class D Results and Simulations 153 -- Chapter 14 Conclusion 173. .
330   $aThis 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.
606   $aAlgorithms
606   $aComputers
606   $aMicroprocessors
606   $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021
606   $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008
606   $aProcessor Architectures$3https://scigraph.springernature.com/ontologies/product-market-codes/I13014
615  0$aAlgorithms.
615  0$aComputers.
615  0$aMicroprocessors.
615 14$aAlgorithm Analysis and Problem Complexity.
615 24$aInformation Systems and Communication Service.
615 24$aProcessor Architectures.
676   $a658.53
676   $a004
700   $aSedighi$b Art$4aut$4http://id.loc.gov/vocabulary/relators/aut$01062602
702   $aSmith$b Milton$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910337560803321
996   $aFair Scheduling in High Performance Computing Environments$92526864
997   $aUNINA