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<article article-type="Research Article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher">journal-of-information-and-optimization-sciences</journal-id>
      <journal-title-group>
        <journal-title>Journal of Information and Optimization Sciences</journal-title>
      </journal-title-group>
      <issn publication-format="electronic">2169-0103</issn>
      <issn publication-format="print">0252-2667</issn>
      <publisher>
        <publisher-name>Taru Publications</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.47974/JIOS-2270</article-id>
      <title-group>
        <article-title>Implementing machine learning for optimized resource allocation in cloud computing environments</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Chaudhari</surname>
            <given-names>Leena Bharat</given-names>
          </name>
          <aff>Department of Electronics and Telecommunication, Lavale, Bharati Vidyapeeth College of Engineering, Pune, Maharashtra, 412115, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Pawar</surname>
            <given-names>Manisha Sagar</given-names>
          </name>
          <aff>Department of Engineering Science and Humanities, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Bhardwaj</surname>
            <given-names>Anjali</given-names>
          </name>
          <aff>Department of Computer Science &amp; Engineering, Noida International University, Greater Noida, Uttar Pradesh, 203201, India</aff>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Soni</surname>
            <given-names>Mahesh</given-names>
          </name>
          <aff>Nassau Financial Group, Hartford, Connecticut, 06102, United States of America</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Asudani</surname>
            <given-names>Deepak Suresh</given-names>
          </name>
          <aff>Department of Computer Science and Engineering, Nagpur Campus, Symbiosis International (Deemed University), Symbiosis Institute of Technology, Nagpur, Maharashtra, 440008, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Rajashri</surname>
            <given-names>C. K.</given-names>
          </name>
          <aff>Department of Computer Science, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu, 600078, India</aff>
        </contrib>
      </contrib-group>
      <volume>47</volume>
      <issue>5-A</issue>
      <fpage>1791</fpage>
      <lpage>1798</lpage>
      <pub-date date-type="pub">
        <day>23</day>
        <month>04</month>
        <year>2026</year>
      </pub-date>
      <abstract>
        <p>Self-driving cars need to learn how to drive in places that change all the time, like when the weather changes, the traffic patterns change, or the noise from sensors changes. RDS makes guidance more reliable and useful, which helps these cars get ready for and deal with these kinds of unknowns. These methods help self-driving cars get ready for problems, change their routes on the fly, and keep working well even when things go wrong. Compared to traditional methods, the results show improvements of up to 25% in utilisation, ~70% reduction in SLA violations, 23% lower energy consumption, and 24% lower cost. </p>
      </abstract>
      <kwd-group>
        <kwd>Cloud computing</kwd>
        <kwd>Resource allocation</kwd>
        <kwd>Machine learning</kwd>
        <kwd>LSTM</kwd>
        <kwd>Reinforcement learning</kwd>
        <kwd>Hybrid framework</kwd>
        <kwd>SLA compliance</kwd>
        <kwd>Energy efficiency</kwd>
        <kwd>Cost optimization</kwd>
      </kwd-group>
      <custom-meta-group>
        <custom-meta>
          <meta-name>access</meta-name>
          <meta-value>open</meta-value>
        </custom-meta>
        <custom-meta>
          <meta-name>retracted</meta-name>
          <meta-value>no</meta-value>
        </custom-meta>
      </custom-meta-group>
    </article-meta>
  </front>
</article>
