Journal article Open Access

An Energy Efficient Hybrid PSO Algorithm in Cloud Environment

Ranjandeep Kaur Khera; V. K. Banga


DCAT Export

<?xml version='1.0' encoding='utf-8'?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#">
  <rdf:Description rdf:about="https://zenodo.org/record/5595544">
    <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/5595544</dct:identifier>
    <foaf:page rdf:resource="https://zenodo.org/record/5595544"/>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Ranjandeep Kaur Khera</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>Assistant Professor, Department of Computer Science, Khalsa College for Women, Amritsar, Punjab, India</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:creator>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>V. K. Banga</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>Principal and Professor, Department of Electronics &amp; Communication Engineering, Amritsar College of Engineering and Technology, Amritsar, Punjab, India.</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </dct:creator>
    <dct:title>An Energy Efficient Hybrid PSO Algorithm in Cloud Environment</dct:title>
    <dct:publisher>
      <foaf:Agent>
        <foaf:name>Zenodo</foaf:name>
      </foaf:Agent>
    </dct:publisher>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued>
    <dcat:keyword>Cloud Computing, Particle Swarm optimization, Invasive Weed optimization.</dcat:keyword>
    <dct:subject>
      <skos:Concept>
        <skos:prefLabel>2249-8958</skos:prefLabel>
        <skos:inScheme>
          <skos:ConceptScheme>
            <dct:title>issn</dct:title>
          </skos:ConceptScheme>
        </skos:inScheme>
      </skos:Concept>
    </dct:subject>
    <dct:subject>
      <skos:Concept>
        <skos:prefLabel>C4704029320/2020©BEIESP</skos:prefLabel>
        <skos:inScheme>
          <skos:ConceptScheme>
            <dct:title>handle</dct:title>
          </skos:ConceptScheme>
        </skos:inScheme>
      </skos:Concept>
    </dct:subject>
    <schema:sponsor>
      <rdf:Description>
        <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/>
        <foaf:name>Blue Eyes Intelligence Engineering &amp; Sciences Publication (BEIESP)</foaf:name>
        <org:memberOf>
          <foaf:Organization>
            <foaf:name>Publisher</foaf:name>
          </foaf:Organization>
        </org:memberOf>
      </rdf:Description>
    </schema:sponsor>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-02-29</dct:issued>
    <dct:language rdf:resource="http://publications.europa.eu/resource/authority/language/ENG"/>
    <owl:sameAs rdf:resource="https://zenodo.org/record/5595544"/>
    <adms:identifier>
      <adms:Identifier>
        <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/5595544</skos:notation>
        <adms:schemeAgency>url</adms:schemeAgency>
      </adms:Identifier>
    </adms:identifier>
    <dct:relation rdf:resource="http://issn.org/resource/ISSN/2249-8958"/>
    <owl:sameAs rdf:resource="https://doi.org/10.35940/ijeat.C4704.029320"/>
    <dct:description>&lt;p&gt;Algorithms exist to schedule various tasks in real time cloud environment. Nowadays many researchers are trying to schedule heavily loaded situations in real time cloud environment using swarming technique. For such studies many parameters need to be considered like cost of the system, processor latency, number of tasks and so on. With the increase in the number of tasks in the set, processing time also increases. In this situation, processor latency is at peak as the number of tasks increases and system costs increase. So the above mentioned problem is handled by proposing a task scheduler that uses a PSO algorithm to remove the limitations of past studies in a heavily loaded situation. The Particle Swarm optimization (PSO) and Invasive Weed Optimization (IWO) are combined to propose a new technique called the HWO algorithm. The proposed algorithm is recommended for preventive tasks in the single-processor in real-time environment systems.&lt;/p&gt;</dct:description>
    <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/>
    <dct:accessRights>
      <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess">
        <rdfs:label>Open Access</rdfs:label>
      </dct:RightsStatement>
    </dct:accessRights>
    <dct:license rdf:resource="https://creativecommons.org/licenses/by/4.0/legalcode"/>
    <dcat:distribution>
      <dcat:Distribution>
        <dcat:accessURL rdf:resource="https://doi.org/10.35940/ijeat.C4704.029320"/>
        <dcat:byteSize>648955</dcat:byteSize>
        <dcat:downloadURL rdf:resource="https://zenodo.org/record/5595544/files/C4704029320.pdf"/>
        <dcat:mediaType>application/pdf</dcat:mediaType>
      </dcat:Distribution>
    </dcat:distribution>
  </rdf:Description>
</rdf:RDF>
32
13
views
downloads
Views 32
Downloads 13
Data volume 8.4 MB
Unique views 32
Unique downloads 13

Share

Cite as