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  <doi_batch_id>aspg-18-3844-1791423649</doi_batch_id>
  <timestamp>20261008014049</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Intelligent Systems and Internet of Things</full_title>
    <abbrev_title>JISIoT</abbrev_title>
    <issn media_type="print">2769-786X</issn>
    <issn media_type="electronic">2690-6791</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>17</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>DeepBalance: A Deep Reinforcement Learning Framework for Dynamic Load Balancing in Software-Defined Networks</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Ali Abdullah</given_name>
      <surname>Ali</surname>
      <affiliations>
       <institution>
        <institution_name>Minister Office of Higher Education and Scientific Research, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Ghaith Ali</given_name>
      <surname>Hussein</surname>
      <affiliations>
       <institution>
        <institution_name>College of Computer Science and Information Technology, Wasit University, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Bushra Majeed</given_name>
      <surname>Muter</surname>
      <affiliations>
       <institution>
        <institution_name>Ministry of Education, Wasit Education Directorate. Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Oday Ali</given_name>
      <surname>Hassen</surname>
      <affiliations>
       <institution>
        <institution_name>Ministry of Education, Wasit Education Directorate. Iraq; Computer Department, College of Education for Pure Sciences, Wasit University, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Software-Defined Networks (SDNs) offer unparalleled network control flexibility, yet efficient load balancing is still challenging in dynamic environments. DeepBalance is a novel framework presented in this paper, which deploys dynamic load balancing in SDNs using Deep Reinforcement Learning (DRL). Our solution employs a Deep Q-Network (DQN) agent, which learns the optimal routing policies by monitoring network states and being rewarded based on load distribution. DeepBalance continuously tracks link utilization and intelligently reshifts traffic to alleviate congestion and achieve maximal throughput. We employ a comprehensive simulation environment, which emulates actual network conditions and traffic patterns. Experimental results demonstrate that DeepBalance significantly outperforms traditional load balancing techniques, lowering link utilisation variance by 37% and total throughput by 28% over shortest-path routing. The infrastructure adapts with changing traffic patterns automatically without the necessity of manual reconfiguration, thus naturally circumventing hotspots by making forward-looking path decisions. Additionally, our visualizations illustrate how the DRL agent learns over time to distribute network load more evenly across alternative paths. DeepBalance is a strong candidate for autonomous network optimization in future SDN deployments.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>279</first_page>
     <last_page>290</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3844</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
    </ai:program>
    <doi_data>
     <doi>10.54216/JISIoT.170120</doi>
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