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Resource allocation in cloud computing using advanced imperialist competitive algorithm

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 9 No. 4 (2019)

DOI
10.11591/ijece.v9i4.pp3286-3297
ISSN
2088-8708, 2088-8708
Published
2019-08-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Cloud computing makes possible free access to computing resources and high-level services for performing complex calculations and mass storage of information on the Internet. Resource management is one of the most important tasks of cloud providers, which is known as resource allocation. Heterogeneous resources and diverse requests at different time intervals makes it difficult to solve resources allocation problems and is considered as a NP-hard problem. Providing an efficient algorithm for resources allocation to satisfy the cloud providers and customers has always attracted much attention of researchers. Heuristic methods have always introduced as a good model for problem solving. However, most algorithms suffer from early convergence. This paper proposes a new approach based on imperialist competitive algorithm (ICA) which emphasizes the optimization of resource allocation in reducing time, cost and energy consumption. The proposed approach has been able to improve the early convergence of colonial competition algorithm by combining with the Tabu Search Algorithm to achieve an optimal solution at an acceptable time. The evaluated results show more efficiency performance than several relevant effective algorithms.

Collaboration network of the authors

3 authors · 2 co-authorship links · 2 across different organisations

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  • Bozorgmehr University of Qaenat
  • Islamic Azad University, Tehran
  • Islamic Azad University of Birjand
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Zahra DehghaniIslamic Azad University of Birjand1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Resource allocation in cloud computing using advanced imperialist competitive algorithm</dc:title><dc:creator>Javadi-Moghaddam, Seyyed-Mohammad</dc:creator><dc:creator>Alipour, Sara</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;div class="page" title="Page 1"&amp;gt;&amp;lt;div class="layoutArea"&amp;gt;&amp;lt;div class="column"&amp;gt;&amp;lt;p&amp;gt;Cloud computing makes possible free access to computing resources and high-level services for performing complex calculations and mass storage of information on the Internet. Resource management is one of the most important tasks of cloud providers, which is known as resource allocation. Heterogeneous resources and diverse requests at different time intervals makes it difficult to solve resources allocation problems and is considered as a NP-hard problem. Providing an efficient algorithm for resources allocation to satisfy the cloud providers and customers has always attracted much attention of researchers. Heuristic methods have always introduced as a good model for problem solving. However, most algorithms suffer from early convergence. This paper proposes a new approach based on imperialist competitive algorithm (ICA) which emphasizes the optimization of resource allocation in reducing time, cost and energy consumption. The proposed approach has been able to improve the early convergence of colonial competition algorithm by combining with the Tabu Search Algorithm to achieve an optimal solution at an acceptable time. The evaluated results show more efficiency performance than several relevant effective algorithms.&amp;lt;/p&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&amp;lt;/div&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2019-08-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v9i4.pp3286-3297</dc:identifier><dc:identifier>10.11591/ijece.v9i4.pp3286-3297</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 9 No 4 (2019); 3286</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/15276/12925</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>