Prairie Dog Optimization Based Efficient Task Scheduling in the Cloud Computing

In Cloud Computing CC, scheduling the algorithms depicts the significant role of identifying the possible tasks scheduling. An efficient task scheduling is considerable to achieve the cost-effective execution as well as enhance the resource utilization. The task scheduling problem is to classified a...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS) s. 1 - 5
Hlavní autori: Hussein, Abbas Hameed Abdul, Sunil, G, Kotha, Mahesh, Alzubaidi, Laith H., Arunasree, B
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 24.11.2023
Predmet:
Cloud computing
Dogs
Genetic algorithms
Nondeterministic Polynomial
Optimization
Prairie Dog Optimization
Processor scheduling
Scheduling
Task analysis
Task Scheduling
Virtual Machine
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract In Cloud Computing CC, scheduling the algorithms depicts the significant role of identifying the possible tasks scheduling. An efficient task scheduling is considerable to achieve the cost-effective execution as well as enhance the resource utilization. The task scheduling problem is to classified as the Nondeterministic Polynomial (NP)-hard problem. To solve this issue, this research proposed an efficient metaheuristic algorithm named Prairie Dog Optimization (PDO) to enhance the task scheduling behaviour in the cloud. The PDO is proposed to improve the task transmitting performance by the cloud network based on the workload of the cloud resources. The proposed method utilizes four prairie dog activities to attains the two basic optimization phases such as exploration and exploitation. The PDO utilizes the two strategies named burrow and foraging to attains the efficient and effective resource allocation. The PDO is modelled for scheduling and distributing the tasks are developed by utilizing the Virtual Machine (VM) factors, time as well as cost. The proposed PDO method attains better results and it achieves the makespan of 112.65, energy consumption of 90.47 and Degree of Imbalance (DoI) of 1.1 respectively when compared to the existing methods like Particle Swarm Optimization, Antlion Optimization (ALO) and Genetic Algorithm (GA).
AbstractList In Cloud Computing CC, scheduling the algorithms depicts the significant role of identifying the possible tasks scheduling. An efficient task scheduling is considerable to achieve the cost-effective execution as well as enhance the resource utilization. The task scheduling problem is to classified as the Nondeterministic Polynomial (NP)-hard problem. To solve this issue, this research proposed an efficient metaheuristic algorithm named Prairie Dog Optimization (PDO) to enhance the task scheduling behaviour in the cloud. The PDO is proposed to improve the task transmitting performance by the cloud network based on the workload of the cloud resources. The proposed method utilizes four prairie dog activities to attains the two basic optimization phases such as exploration and exploitation. The PDO utilizes the two strategies named burrow and foraging to attains the efficient and effective resource allocation. The PDO is modelled for scheduling and distributing the tasks are developed by utilizing the Virtual Machine (VM) factors, time as well as cost. The proposed PDO method attains better results and it achieves the makespan of 112.65, energy consumption of 90.47 and Degree of Imbalance (DoI) of 1.1 respectively when compared to the existing methods like Particle Swarm Optimization, Antlion Optimization (ALO) and Genetic Algorithm (GA).
Author Hussein, Abbas Hameed Abdul
Kotha, Mahesh
Sunil, G
Alzubaidi, Laith H.
Arunasree, B
Author_xml – sequence: 1
  givenname: Abbas Hameed Abdul
  surname: Hussein
  fullname: Hussein, Abbas Hameed Abdul
  email: Abdul.hussien@abu.edu.iq
  organization: Ahl Al Bayt University,College of Pharmacy,Karbala,Iraq
– sequence: 2
  givenname: G
  surname: Sunil
  fullname: Sunil, G
  email: goli.sunilreddy@gmail.com
  organization: SR University,School of Computer Science & Artificial Intelligence,Warangal,India
– sequence: 3
  givenname: Mahesh
  surname: Kotha
  fullname: Kotha, Mahesh
  email: Kotha.mahesh528@gmail.com
  organization: CMR technical Campus,Department of Computer Science and Engineering,Hyderabad,India
– sequence: 4
  givenname: Laith H.
  surname: Alzubaidi
  fullname: Alzubaidi, Laith H.
  email: laith.h.alzubaidi@gmail.com
  organization: The Islamic university,Najaf,Iraq
– sequence: 5
  givenname: B
  surname: Arunasree
  fullname: Arunasree, B
  email: srikiron@gmail.com
  organization: Malla Reddy Engineering College for Women,Hyderabad,India
BookMark eNo1j8tOwzAURI0ECyj9AxbmAxL8iJt4CaZApEpBallXrn3dXpE4UeIs4OupBKxGmjM60tyQy9hHIOSes5xzph9qU9dmq7TWMhdMyJyzQnC1EhdkqUtdScUkV4VS16R5Hy2OCPS5P9JmSNjht03YR_pkJ_B0HQI6hJjozk6fdOtO4OcW45FipOkE1LT97Knpu2FO5_qWXAXbTrD8ywX5eFnvzFu2aV5r87jJkHOdMhGUBFdVhbNgq1XpleNQWihDKQ9ennGldeBWWBY8k0FocXAFD05qdh4puSB3v14EgP0wYmfHr_3_T_kDDVROVw
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ICIICS59993.2023.10421562
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Xplore
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Xplore
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9798350315455
EndPage 5
ExternalDocumentID 10421562
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i119t-2f53ec884caea867d5c1e7ae7f73bd32f5899f1a2a0fd03f292bc41fc390e7f53
IEDL.DBID RIE
IngestDate Wed May 01 11:58:52 EDT 2024
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i119t-2f53ec884caea867d5c1e7ae7f73bd32f5899f1a2a0fd03f292bc41fc390e7f53
PageCount 5
ParticipantIDs ieee_primary_10421562
PublicationCentury 2000
PublicationDate 2023-Nov.-24
PublicationDateYYYYMMDD 2023-11-24
PublicationDate_xml – month: 11
  year: 2023
  text: 2023-Nov.-24
  day: 24
PublicationDecade 2020
PublicationTitle 2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS)
PublicationTitleAbbrev ICIICS
PublicationYear 2023
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.8665762
Snippet In Cloud Computing CC, scheduling the algorithms depicts the significant role of identifying the possible tasks scheduling. An efficient task scheduling is...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Cloud computing
Dogs
Genetic algorithms
Nondeterministic Polynomial
Optimization
Prairie Dog Optimization
Processor scheduling
Scheduling
Task analysis
Task Scheduling
Virtual Machine
Title Prairie Dog Optimization Based Efficient Task Scheduling in the Cloud Computing
URI https://ieeexplore.ieee.org/document/10421562
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwEA86RHxSceI3EXztXNu0aV6tGw5kG2zC3kY-LqOorXSbf7-XrFN88MG3kBwXuEtyueR-d4TcKa3cr3oS4FrJAgaaB8IqwMOQCTDCgXY9UPiZD4fZbCbGDVjdY2EAwAefQcc1_V--qfTaPZXhDmdoodyJu8t5ugFr7ZPbJm_m_SAfDPJJgleeuOOqgne29L8qp3jD0T_855RHpP0DwaPjb-NyTHagPCGjcS0L9G3pY7WgI9zs7w2Kkj6gMTK05_NBIEM6lctXOkGFGBdpvqBFSfGmR_O3am3oppIDdrfJS783zZ-CpiJCUIShWAWRTWLQWca0BJml3CQ6BC6BWx4rE-Mwuk82lJHsWtONbSQipVlodSy6SJTEp6RVViWcEcqltqm1yEUKphRIUCyzSSS1Qhcjjc5J20lj_rFJejHfCuLij_5LcuBk7mB6EbsirVW9hmuypz9XxbK-8ar6AlUKl5I
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDI7QQMAJEEO8CRLXjjZN1-ZK2bSKsU3akHab8nCmCljRHvx-nK4DceDALXJekhPHcZzPJuROaeW86pGHeyXxOOjYE1YBHoZcgBEOtFsChbtxr5eMx2JQgdVLLAwAlJ_PoOGKpS_fFHrlnspQwjlqKHfibkecM38N19olt1XkzPsszbJ0GOGlJ2y4vOCNTY9fuVNK1dE--Oekh6T-A8Kjg2_1ckS2YHZM-oO5zNG6pY_FlPZR3N8rHCV9QHVkaKuMCIED0pFcvNIhLolxf82nNJ9RvOvR9K1YGbrO5YDkOnlpt0Zpx6tyInh5EIilx2wUgk4SriXIpBmbSAcQS4htHCoTYjUaUDaQTPrW-KFlginNA6tD4WOjKDwhtVkxg1NCY6lt01ocRQquFEhQPLERk1qhkdFkZ6TuuDH5WIe9mGwYcf4H_YbsdUbP3Uk36z1dkH3HfwfaY_yS1JbzFVyRHf25zBfz63LZvgBlj5rZ
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=2023+International+Conference+on+Integrated+Intelligence+and+Communication+Systems+%28ICIICS%29&rft.atitle=Prairie+Dog+Optimization+Based+Efficient+Task+Scheduling+in+the+Cloud+Computing&rft.au=Hussein%2C+Abbas+Hameed+Abdul&rft.au=Sunil%2C+G&rft.au=Kotha%2C+Mahesh&rft.au=Alzubaidi%2C+Laith+H.&rft.date=2023-11-24&rft.pub=IEEE&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FICIICS59993.2023.10421562&rft.externalDocID=10421562