Hybrid Ant Colony-Particle Swarm Optimization for Dynamic Resource Allocation in Cloud Data Centers

Effective use of computational resources is a very challenging issue in cloud data centres as demands from users are very high. However, classical optimization methods are often not able to cope with changing workloads, which means they can yield to inefficient decisions. A Hybrid Optimization Algor...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Journal of Al-Qadisiyah for Computer Science and Mathematics Ročník 17; číslo 3
Hlavný autor: Abdulrazzak Ahmed, Hiba
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: 30.09.2025
ISSN:2074-0204, 2521-3504
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract Effective use of computational resources is a very challenging issue in cloud data centres as demands from users are very high. However, classical optimization methods are often not able to cope with changing workloads, which means they can yield to inefficient decisions. A Hybrid Optimization Algorithm based on PSO Ant Colony algorithm hybrid PSO–ACO is presented in this paper for the purpose of optimizing resource allocation efficiency in cloud environment. In this hybrid model, the heuristic search ability of ACO and exploitative nature of PSO is synergized to deliver the best heuristics to meet the demands of dynamic resource provisioning with minimum energy consumption, reduced SLA violation and improved load balancing. The results supported that the hybrid PSO–ACO algorithm achieves the highest resource efficiency with reduces execution time and SLA violations, balances load effectively and reaches optimal solutions quickly and stably and this means that the hybrid ACO-PSO approach clearly outperforms both ACO and PSO individually in all performance indicators, making it the best choice for dynamic cloud computing systems.
AbstractList Effective use of computational resources is a very challenging issue in cloud data centres as demands from users are very high. However, classical optimization methods are often not able to cope with changing workloads, which means they can yield to inefficient decisions. A Hybrid Optimization Algorithm based on PSO Ant Colony algorithm hybrid PSO–ACO is presented in this paper for the purpose of optimizing resource allocation efficiency in cloud environment. In this hybrid model, the heuristic search ability of ACO and exploitative nature of PSO is synergized to deliver the best heuristics to meet the demands of dynamic resource provisioning with minimum energy consumption, reduced SLA violation and improved load balancing. The results supported that the hybrid PSO–ACO algorithm achieves the highest resource efficiency with reduces execution time and SLA violations, balances load effectively and reaches optimal solutions quickly and stably and this means that the hybrid ACO-PSO approach clearly outperforms both ACO and PSO individually in all performance indicators, making it the best choice for dynamic cloud computing systems.
Author Abdulrazzak Ahmed, Hiba
Author_xml – sequence: 1
  givenname: Hiba
  surname: Abdulrazzak Ahmed
  fullname: Abdulrazzak Ahmed, Hiba
BookMark eNot0N1KwzAYBuAgE5xzd-BBbqA1v21yODp1wmCiOw9pmkCkTWbSIfXq3Y9H3wvfy3vw3INZiMEC8IhRSSRF7Onr2-ShJIjwEtclJbQWN2BOOMEF5YjNThnVrEAEsTuwzNm3iLGaY1mhOTCbqU2-g6swwib2MUzFu06jN72Fnz86DXB3GP3gf_XoY4AuJriegh68gR82x2MyFq76Pprr3wfY9PHYwbUeNWxsGG3KD-DW6T7b5f9dgP3L877ZFNvd61uz2hZGYlHUBjPbSoQk5sZKqVvrhCZYmEoTWlHBKudOTeuIrjsmuOS8xVZQ1tGWCE0XgF1nTYo5J-vUIflBp0lhpC5U6kKlzlQK1-pCRf8AmExglA
ContentType Journal Article
DBID AAYXX
CITATION
DOI 10.29304/jqcsm.2025.17.32378
DatabaseName CrossRef
DatabaseTitle CrossRef
DatabaseTitleList CrossRef
DeliveryMethod fulltext_linktorsrc
EISSN 2521-3504
ExternalDocumentID 10_29304_jqcsm_2025_17_32378
GroupedDBID AAYXX
ALMA_UNASSIGNED_HOLDINGS
CITATION
OK1
ID FETCH-LOGICAL-c918-7c14eb900915ce99abef8a218c6a2363846ffc91ef2a7d485955b1e834d3b28a3
ISSN 2074-0204
IngestDate Wed Oct 29 21:08:31 EDT 2025
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed false
IsScholarly false
Issue 3
Language English
License http://creativecommons.org/licenses/by-nc-nd/4.0
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-c918-7c14eb900915ce99abef8a218c6a2363846ffc91ef2a7d485955b1e834d3b28a3
OpenAccessLink https://jqcsm.qu.edu.iq/index.php/journalcm/article/download/2378/1093
ParticipantIDs crossref_primary_10_29304_jqcsm_2025_17_32378
PublicationCentury 2000
PublicationDate 2025-09-30
PublicationDateYYYYMMDD 2025-09-30
PublicationDate_xml – month: 09
  year: 2025
  text: 2025-09-30
  day: 30
PublicationDecade 2020
PublicationTitle Journal of Al-Qadisiyah for Computer Science and Mathematics
PublicationYear 2025
SSID ssib044751960
ssib016479590
ssib032177102
ssib046619541
Score 1.9230548
Snippet Effective use of computational resources is a very challenging issue in cloud data centres as demands from users are very high. However, classical optimization...
SourceID crossref
SourceType Index Database
Title Hybrid Ant Colony-Particle Swarm Optimization for Dynamic Resource Allocation in Cloud Data Centers
Volume 17
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2521-3504
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssib044751960
  issn: 2074-0204
  databaseCode: M~E
  dateStart: 20090101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Pb9MwFLbK4MBlGgIEDJAP3CqPJrbr-FiNTb0whuhht8h2HC0izUZ_jK0H_nae7TgNU4XYgYtVudZr2vfp82f3_UDog-Y6o1RLQscjRgAUBVGScRiEZUzIkTGlbzYhzs6yiwt5PhhsYi7MTS2aJru9ldf_1dUwB852qbMPcHdnFCbgNTgdRnA7jP_k-OmdS8JyVQHcrQAc7sl5u2r47adazIdfgCXmbfqljzL8FLrSd1f5w0nttrgYBnlcX60LgMdK-bvgNmR-l6KtyVdVVMvqTl16w7FlRMcgIaojFort5PxEF-t6oTYb9X04uZyH-9dppVX_TiLlMYAiUlfqojxd2m3YZcIcCAVCeTsXuVf0MEZ3UTrIkRFznP7DLF3lgJQfJeKIpjR0_vmzgva9na2LN4STjreTeyu5s5InIvdWHqHHqeDSMeLnXyeRi1yZNcm3_yBSOLqJXqk1VycRyKt7n4HOkdw3Su2-fMjU9B_8ccfj95RQT9LMDtB-6zk8Ceh4hga2eY5MwA8G_OB7-MEeP7iPHwxuxi1-cMQP3uIHVw32-MEOP7jFzws0Oz2ZHU9J24mDGJlkRJiEWS1BjifcWCmVtmWmQByasUopMDgblyWstGWqRMFcyTyuE5tRVlCdZoq-RHvNVWNfIeyaJhqTlUKAFrYiU1rCiVeNS1UwzYx5jUj8UfLrUG8l_5v33jxw_SF6ugXsW7S3WqztO_TE3Kyq5eK9h8BvChh3Bw
linkProvider ISSN International Centre
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%3Ajournal&rft.genre=article&rft.atitle=Hybrid+Ant+Colony-Particle+Swarm+Optimization+for+Dynamic+Resource+Allocation+in+Cloud+Data+Centers&rft.jtitle=Journal+of+Al-Qadisiyah+for+Computer+Science+and+Mathematics&rft.au=Abdulrazzak+Ahmed%2C+Hiba&rft.date=2025-09-30&rft.issn=2074-0204&rft.eissn=2521-3504&rft.volume=17&rft.issue=3&rft_id=info:doi/10.29304%2Fjqcsm.2025.17.32378&rft.externalDBID=n%2Fa&rft.externalDocID=10_29304_jqcsm_2025_17_32378
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2074-0204&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2074-0204&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2074-0204&client=summon