Gath-Geva clustering algorithm for high performance computing (HPC) monitoring

Supercomputer is a fundamental means to perform complex and huge computations. Simultaneously, it is one of the most energy consuming infrastructures. The diversity of applications executed within a HPC system makes it difficult to control resource utilization and identify the behaviour of these app...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS) S. 1 - 6
Hauptverfasser: El Motaki, Saloua, Yahyaouy, Ali, Gualous, Hamid, Sabor, Jalal
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2019
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract Supercomputer is a fundamental means to perform complex and huge computations. Simultaneously, it is one of the most energy consuming infrastructures. The diversity of applications executed within a HPC system makes it difficult to control resource utilization and identify the behaviour of these applications while running. To effectively alleviate this concern, scientists have appealed to use machine learning techniques for HPC monitoring and diagnosis. This work focuses on the employment of Gath-Geva clustering algorithm to identify applications and their behavioural similarities while running on HPC system. The choice of this algorithm is based on its ability to be adapted to data structures of arbitrarily shaped, sized and dense data.
AbstractList Supercomputer is a fundamental means to perform complex and huge computations. Simultaneously, it is one of the most energy consuming infrastructures. The diversity of applications executed within a HPC system makes it difficult to control resource utilization and identify the behaviour of these applications while running. To effectively alleviate this concern, scientists have appealed to use machine learning techniques for HPC monitoring and diagnosis. This work focuses on the employment of Gath-Geva clustering algorithm to identify applications and their behavioural similarities while running on HPC system. The choice of this algorithm is based on its ability to be adapted to data structures of arbitrarily shaped, sized and dense data.
Author Gualous, Hamid
Sabor, Jalal
El Motaki, Saloua
Yahyaouy, Ali
Author_xml – sequence: 1
  givenname: Saloua
  surname: El Motaki
  fullname: El Motaki, Saloua
  email: saloua.ehnotaki@usmba.ac.ma
  organization: University Sidi Mohamed Ben Abdellah,LIIAN Laboratory, Computer Science depart., FSDM,Fez,Morocco
– sequence: 2
  givenname: Ali
  surname: Yahyaouy
  fullname: Yahyaouy, Ali
  email: ayahyaouy@yahoo.fr
  organization: University Sidi Mohamed Ben Abdellah,LIIAN Laboratory, Computer Science depart., FSDM,Fez,Morocco
– sequence: 3
  givenname: Hamid
  surname: Gualous
  fullname: Gualous, Hamid
  email: hamid.gualous@unicaen.fr
  organization: University of Caen Normandy,Applied Sciences Laboratory,Caen,France
– sequence: 4
  givenname: Jalal
  surname: Sabor
  fullname: Sabor, Jalal
  email: j.sabor@ensam.umi.ac.ma
  organization: ENSAM-Engineering School CP2S Laboratory,Meknes,Morocco
BookMark eNotj09Lw0AUxFfQg9Z-AkH2qIfE95Jskj1KtH-gqKCey-vmJVlIsmG7Ffz2tlgYmDn8ZmBuxOXoRhbiHiFGBP20rl4-swIgixNAHZc6S1INF2KuixKLpEQASPNr8bak0EVL_iFp-sM-sLdjK6lvnbehG2TjvOxs28mJ_TEPNBqWxg3TIZzAh9VH9SgHN9rgTs1bcdVQv-f52Wfie_H6Va2izftyXT1vIouFClFhMkCVGlaKqDa5rpuMmhoYmHPQJTInCjWZBNSuMVjninLAmo7aKeB0Ju7-dy0zbydvB_K_2_PL9A_XHE2Y
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ICDS47004.2019.8942390
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Xplore Digital Library
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Xplore Digital Library
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9781728100036
1728100038
EndPage 6
ExternalDocumentID 8942390
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i175t-7c40153ce55aadc69df4afd0e0ee60981ee2519ac205bfc1d65a601da1dab50e3
IEDL.DBID RIE
ISICitedReferencesCount 1
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000698200200080&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Sep 10 07:40:49 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i175t-7c40153ce55aadc69df4afd0e0ee60981ee2519ac205bfc1d65a601da1dab50e3
PageCount 6
ParticipantIDs ieee_primary_8942390
PublicationCentury 2000
PublicationDate 2019-Oct.
PublicationDateYYYYMMDD 2019-10-01
PublicationDate_xml – month: 10
  year: 2019
  text: 2019-Oct.
PublicationDecade 2010
PublicationTitle 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS)
PublicationTitleAbbrev INTELCOMPDS
PublicationYear 2019
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.7277795
Snippet Supercomputer is a fundamental means to perform complex and huge computations. Simultaneously, it is one of the most energy consuming infrastructures. The...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Application identification
Clustering algorithms
Gath-Geva clustering
HPC
Machine learning algorithms
Measurement
Monitoring
Partitioning algorithms
Quality of service
Resilience
Resource management
Shape
Supercomputer
Supercomputers
Title Gath-Geva clustering algorithm for high performance computing (HPC) monitoring
URI https://ieeexplore.ieee.org/document/8942390
WOSCitedRecordID wos000698200200080&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA61ePCk0opvcvCgYOzGbV7naltBSsEHvZVsMtGCfVDb_n4z2dIieBH2sOzOEpjZZebLzvcNIVda5bkqvGIGILAmADArg2AqD8C51_cqzVh6f1a9nh4MTL9CbjdcmGidms_gDk_Tv3w_dUvcKmtog3J1EaDvKCVLrtaa9Msz03hqPbw0Ua0dG7biG1Aa_5qakpJGe_9_yx2Q-pZ9R_ubvHJIKjCpkV4nlmqsAytL3dcS1Q3iPWq_PqYR3X-Oaaw9KUoP09mWCUBdGtmAhtfdfuuGjtMHjE_WyVv78bXVZetZCGwUE_yCKReBkMgdCGGtd9L40LTBZ5AByMxoDoAcVBRcFEVw3EthI9byNh6FyCA_ItXJdALHhBYAintuHbJKA9hYIjoZoyR1sMF4fUJq6IvhrJS7GK7dcPr35TOyh-4u-9vOSXUxX8IF2XWrxeh7fpli9AOPSpbP
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS8MwEA5jCvqksom_zYMPCsY1dmnS5-l-4CwDp-xtpMlFB1s35ra_31xXNgRfhDyU5Erh0nJ3zX3fR8iNkmEoUytZDOBYHQCYjpxgMnTAuVWPMtdY-ujKJFGDQdwrkfsNFsZb581n8ICX-Vm-nZol_iqrqRjp6nyBvoPKWQVaq4D98iCudRpPb3Xka8eWLf8OrM1_6abkYaN58L8HHpLqFn9He5vIckRKkFVI0vLJGmvBSlMzXiK_gV-jevw59fX914T67JMi-TCdbbEA1OSiDWh42-417ugk_4Txzip5bz73G21WqCGwkQ_xCyaNL4VEaEAIra2JYuvq2tkAAoAoiBUHQBQqUi6K1BluI6F9tWW1H6kIIDwm5WyawQmhKYDklmuDuFIH2ieJJvL7FCmnXWzVKamgL4azNeHFsHDD2d_T12Sv3X_tDrud5OWc7KPr191uF6S8mC_hkuya1WL0Pb_K9-sHmEmaGA
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2019+Third+International+Conference+on+Intelligent+Computing+in+Data+Sciences+%28ICDS%29&rft.atitle=Gath-Geva+clustering+algorithm+for+high+performance+computing+%28HPC%29+monitoring&rft.au=El+Motaki%2C+Saloua&rft.au=Yahyaouy%2C+Ali&rft.au=Gualous%2C+Hamid&rft.au=Sabor%2C+Jalal&rft.date=2019-10-01&rft.pub=IEEE&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FICDS47004.2019.8942390&rft.externalDocID=8942390