<?xml version="1.0" encoding="utf-8"?>
<TEI xmlns="http://www.tei-c.org/ns/1.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:hal="http://hal.archives-ouvertes.fr/" xmlns:gml="http://www.opengis.net/gml/3.3/" xmlns:gmlce="http://www.opengis.net/gml/3.3/ce" version="1.1" xsi:schemaLocation="http://www.tei-c.org/ns/1.0 http://api.archives-ouvertes.fr/documents/aofr-sword.xsd">
  <teiHeader>
    <fileDesc>
      <titleStmt>
        <title>HAL TEI export of hal-03266462</title>
      </titleStmt>
      <publicationStmt>
        <distributor>CCSD</distributor>
        <availability status="restricted">
          <licence target="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0 - Universal</licence>
        </availability>
        <date when="2026-05-17T21:41:08+02:00"/>
      </publicationStmt>
      <sourceDesc>
        <p part="N">HAL API Platform</p>
      </sourceDesc>
    </fileDesc>
  </teiHeader>
  <text>
    <body>
      <listBibl>
        <biblFull>
          <titleStmt>
            <title xml:lang="en">DeepRoute: Herding Elephant and Mice Flows with Reinforcement Learning</title>
            <author role="aut">
              <persName>
                <forename type="first">Mariam</forename>
                <surname>Kiran</surname>
              </persName>
              <email type="md5">a47bbb671d33730766d428d82dc7335f</email>
              <email type="domain">lbl.gov</email>
              <idno type="idhal" notation="numeric">1102791</idno>
              <idno type="halauthorid" notation="string">2233588-1102791</idno>
              <affiliation ref="#struct-82005"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Bashir</forename>
                <surname>Mohammed</surname>
              </persName>
              <email type="md5">bf91b552ea382c96e18d2e353d245705</email>
              <email type="domain">lbl.gov</email>
              <idno type="idhal" notation="numeric">1102792</idno>
              <idno type="halauthorid" notation="string">2233589-1102792</idno>
              <affiliation ref="#struct-82005"/>
              <affiliation ref="#struct-141072"/>
            </author>
            <author role="aut">
              <persName>
                <forename type="first">Nandini</forename>
                <surname>Krishnaswamy</surname>
              </persName>
              <email type="md5">f6a179396e09cf55651c3f47f42e703e</email>
              <email type="domain">lbl.gov</email>
              <idno type="idhal" notation="numeric">1102793</idno>
              <idno type="halauthorid" notation="string">2233590-1102793</idno>
              <affiliation ref="#struct-82005"/>
              <affiliation ref="#struct-141072"/>
            </author>
            <editor role="depositor">
              <persName>
                <forename>Hal</forename>
                <surname>Ifip</surname>
              </persName>
              <email type="md5">2073ac78024b6e13f2714db96e9b1e63</email>
              <email type="domain">inria.fr</email>
            </editor>
          </titleStmt>
          <editionStmt>
            <edition n="v1" type="current">
              <date type="whenSubmitted">2021-06-21 17:31:49</date>
              <date type="whenModified">2025-08-26 15:21:01</date>
              <date type="whenReleased">2021-06-21 17:41:16</date>
              <date type="whenProduced">2019-12-03</date>
              <date type="whenEndEmbargoed">2023-01-01</date>
              <ref type="file" target="https://inria.hal.science/hal-03266462v1/document">
                <date notBefore="2023-01-01"/>
              </ref>
              <ref type="file" subtype="author" n="1" target="https://inria.hal.science/hal-03266462v1/file/487577_1_En_20_Chapter.pdf" id="file-3266462-2870226">
                <date notBefore="2023-01-01"/>
              </ref>
            </edition>
            <respStmt>
              <resp>contributor</resp>
              <name key="200187">
                <persName>
                  <forename>Hal</forename>
                  <surname>Ifip</surname>
                </persName>
                <email type="md5">2073ac78024b6e13f2714db96e9b1e63</email>
                <email type="domain">inria.fr</email>
              </name>
            </respStmt>
          </editionStmt>
          <publicationStmt>
            <distributor>CCSD</distributor>
            <idno type="halId">hal-03266462</idno>
            <idno type="halUri">https://inria.hal.science/hal-03266462</idno>
            <idno type="halBibtex">kiran:hal-03266462</idno>
            <idno type="halRefHtml">&lt;i&gt;MLN 2019 - 2nd International Conference on Machine Learning for Networking&lt;/i&gt;, Dec 2019, Paris, France. pp.296-314, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-030-45778-5_20"&gt;&amp;#x27E8;10.1007/978-3-030-45778-5_20&amp;#x27E9;&lt;/a&gt;</idno>
            <idno type="halRef">MLN 2019 - 2nd International Conference on Machine Learning for Networking, Dec 2019, Paris, France. pp.296-314, &amp;#x27E8;10.1007/978-3-030-45778-5_20&amp;#x27E9;</idno>
            <availability status="restricted">
              <licence target="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 - Attribution<ref corresp="#file-3266462-2870226"/></licence>
            </availability>
          </publicationStmt>
          <seriesStmt>
            <idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno>
            <idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno>
            <idno type="stamp" n="INRIA-SOPHIA">INRIA Sophia Antipolis - Méditerranée</idno>
            <idno type="stamp" n="INRIASO">INRIA-SOPHIA</idno>
            <idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno>
            <idno type="stamp" n="INRIA34">Antenne Inria de l'université de Montpellier</idno>
            <idno type="stamp" n="TESTALAIN1">TESTALAIN1</idno>
            <idno type="stamp" n="IFIP-LNCS" corresp="IFIP">IFIP - Lecture Notes in Computer Science</idno>
            <idno type="stamp" n="IFIP">IFIP - International Federation for Information Processing</idno>
            <idno type="stamp" n="ZENITH" corresp="LIRMM">Scientific Data Management</idno>
            <idno type="stamp" n="LIRMM">Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier</idno>
            <idno type="stamp" n="INRIA2">INRIA 2</idno>
            <idno type="stamp" n="IFIP-TC" corresp="IFIP">IFIP Technical Committees </idno>
            <idno type="stamp" n="IFIP-TC6" corresp="IFIP-TC">TC 6: Communication Systems</idno>
            <idno type="stamp" n="MIPS">Mathématiques, Informatique, Physique et Systèmes</idno>
            <idno type="stamp" n="UNIV-MONTPELLIER">Université de Montpellier</idno>
            <idno type="stamp" n="UNIV-COTEDAZUR">Université Côte d'Azur</idno>
            <idno type="stamp" n="IFIP-LNCS-12081" corresp="IFIP-LNCS">Machine Learning for Networking</idno>
            <idno type="stamp" n="IFIP-MLN" corresp="IFIP">Machine Learning for Networking</idno>
            <idno type="stamp" n="UM-2015-2021" corresp="UNIV-MONTPELLIER">Université de Montpellier (2015-2021)</idno>
            <idno type="stamp" n="INRIA-ETATSUNIS">Copublications Inria-Etats-Unis</idno>
          </seriesStmt>
          <notesStmt>
            <note type="audience" n="2">International</note>
            <note type="invited" n="0">No</note>
            <note type="popular" n="0">No</note>
            <note type="peer" n="1">Yes</note>
            <note type="proceedings" n="1">Yes</note>
          </notesStmt>
          <sourceDesc>
            <biblStruct>
              <analytic>
                <title xml:lang="en">DeepRoute: Herding Elephant and Mice Flows with Reinforcement Learning</title>
                <author role="aut">
                  <persName>
                    <forename type="first">Mariam</forename>
                    <surname>Kiran</surname>
                  </persName>
                  <email type="md5">a47bbb671d33730766d428d82dc7335f</email>
                  <email type="domain">lbl.gov</email>
                  <idno type="idhal" notation="numeric">1102791</idno>
                  <idno type="halauthorid" notation="string">2233588-1102791</idno>
                  <affiliation ref="#struct-82005"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Bashir</forename>
                    <surname>Mohammed</surname>
                  </persName>
                  <email type="md5">bf91b552ea382c96e18d2e353d245705</email>
                  <email type="domain">lbl.gov</email>
                  <idno type="idhal" notation="numeric">1102792</idno>
                  <idno type="halauthorid" notation="string">2233589-1102792</idno>
                  <affiliation ref="#struct-82005"/>
                  <affiliation ref="#struct-141072"/>
                </author>
                <author role="aut">
                  <persName>
                    <forename type="first">Nandini</forename>
                    <surname>Krishnaswamy</surname>
                  </persName>
                  <email type="md5">f6a179396e09cf55651c3f47f42e703e</email>
                  <email type="domain">lbl.gov</email>
                  <idno type="idhal" notation="numeric">1102793</idno>
                  <idno type="halauthorid" notation="string">2233590-1102793</idno>
                  <affiliation ref="#struct-82005"/>
                  <affiliation ref="#struct-141072"/>
                </author>
              </analytic>
              <monogr>
                <title level="m">Lecture Notes in Computer Science</title>
                <meeting>
                  <title>MLN 2019 - 2nd International Conference on Machine Learning for Networking</title>
                  <date type="start">2019-12-03</date>
                  <date type="end">2019-12-05</date>
                  <settlement>Paris</settlement>
                  <country key="FR">France</country>
                </meeting>
                <editor>Selma Boumerdassi</editor>
                <editor>Éric Renault</editor>
                <editor>Paul Mühlethaler</editor>
                <imprint>
                  <publisher>Springer International Publishing</publisher>
                  <biblScope unit="serie">Machine Learning for Networking</biblScope>
                  <biblScope unit="volume">LNCS-12081</biblScope>
                  <biblScope unit="pp">296-314</biblScope>
                  <date type="datePub">2020</date>
                </imprint>
              </monogr>
              <idno type="doi">10.1007/978-3-030-45778-5_20</idno>
            </biblStruct>
          </sourceDesc>
          <profileDesc>
            <langUsage>
              <language ident="en">English</language>
            </langUsage>
            <textClass>
              <keywords scheme="author">
                <term xml:lang="en">Route optimization</term>
                <term xml:lang="en">Reinforcement learning</term>
                <term xml:lang="en">Path computation</term>
              </keywords>
              <classCode scheme="halDomain" n="info">Computer Science [cs]</classCode>
              <classCode scheme="halDomain" n="info.info-ni">Computer Science [cs]/Networking and Internet Architecture [cs.NI]</classCode>
              <classCode scheme="halTypology" n="COMM">Conference papers</classCode>
              <classCode scheme="halOldTypology" n="COMM">Conference papers</classCode>
              <classCode scheme="halTreeTypology" n="COMM">Conference papers</classCode>
            </textClass>
            <abstract xml:lang="en">
              <p>Wide area networks are built to have enough resilience and flexibility, such as offering many paths between multiple pairs of end-hosts. To prevent congestion, current practices involve numerous tweaking of routing tables to optimize path computation, such as flow diversion to alternate paths or load balancing. However, this process is slow, costly and require difficult online decision-making to learn appropriate settings, such as flow arrival rate, workload, and current network environment. Inspired by recent advances in AI to manage resources, we present DeepRoute, a model-less reinforcement learning approach that translates the path computation problem to a learning problem. Learning from the network environment, DeepRoute learns strategies to manage arriving elephant and mice flows to improve the average path utilization in the network. Comparing to other strategies such as prioritizing certain flows and random decisions, DeepRoute is shown to improve average network path utilization to 30% and potentially reduce possible congestion across the whole network. This paper presents results in simulation and also how DeepRoute can be demonstrated by a Mininet implementation.</p>
            </abstract>
            <particDesc>
              <org type="consortium">TC 6</org>
            </particDesc>
          </profileDesc>
        </biblFull>
      </listBibl>
    </body>
    <back>
      <listOrg type="structures">
        <org type="institution" xml:id="struct-82005" status="VALID">
          <idno type="ROR">https://ror.org/02jbv0t02</idno>
          <orgName>Lawrence Berkeley National Laboratory [Berkeley]</orgName>
          <orgName type="acronym">LBNL</orgName>
          <desc>
            <address>
              <addrLine>1 Cyclotron Rd. MS 50A-1148, Berkeley, CA 94720</addrLine>
              <country key="US"/>
            </address>
            <ref type="url">http://www.lbl.gov</ref>
          </desc>
        </org>
        <org type="researchteam" xml:id="struct-141072" status="OLD">
          <idno type="RNSR">201121208J</idno>
          <orgName>Scientific Data Management</orgName>
          <orgName type="acronym">ZENITH</orgName>
          <date type="end">2021-12-31</date>
          <desc>
            <address>
              <addrLine>LIRMM, 161 rue Ada, 34000 Montpellier</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">https://team.inria.fr/zenith/</ref>
          </desc>
          <listRelation>
            <relation active="#struct-181" type="direct"/>
            <relation name="UMR5506" active="#struct-410122" type="indirect"/>
            <relation name="UMR5506" active="#struct-441569" type="indirect"/>
            <relation active="#struct-34586" type="direct"/>
            <relation active="#struct-300009" type="indirect"/>
          </listRelation>
        </org>
        <org type="laboratory" xml:id="struct-181" status="OLD">
          <idno type="IdRef">139590827</idno>
          <idno type="ISNI">0000000405990488</idno>
          <idno type="RNSR">199111950H</idno>
          <idno type="ROR">https://ror.org/013yean28</idno>
          <orgName>Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier</orgName>
          <orgName type="acronym">LIRMM</orgName>
          <date type="start">1995-01-01</date>
          <date type="end">2021-12-31</date>
          <desc>
            <address>
              <addrLine>161 rue Ada - 34095 Montpellier</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.lirmm.fr</ref>
          </desc>
          <listRelation>
            <relation name="UMR5506" active="#struct-410122" type="direct"/>
            <relation name="UMR5506" active="#struct-441569" type="direct"/>
          </listRelation>
        </org>
        <org type="institution" xml:id="struct-410122" status="OLD">
          <idno type="ISNI">0000000120970141</idno>
          <idno type="ROR">https://ror.org/051escj72</idno>
          <orgName>Université de Montpellier</orgName>
          <orgName type="acronym">UM</orgName>
          <date type="end">2021-12-31</date>
          <desc>
            <address>
              <addrLine>163 rue Auguste Broussonnet - 34090 Montpellier</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.umontpellier.fr/</ref>
          </desc>
        </org>
        <org type="regroupinstitution" xml:id="struct-441569" status="VALID">
          <idno type="IdRef">02636817X</idno>
          <idno type="ISNI">0000000122597504</idno>
          <idno type="ROR">https://ror.org/02feahw73</idno>
          <orgName>Centre National de la Recherche Scientifique</orgName>
          <orgName type="acronym">CNRS</orgName>
          <date type="start">1939-10-19</date>
          <desc>
            <address>
              <country key="FR"/>
            </address>
            <ref type="url">https://www.cnrs.fr/</ref>
          </desc>
        </org>
        <org type="laboratory" xml:id="struct-34586" status="VALID">
          <idno type="RNSR">198318250R</idno>
          <idno type="ROR">https://ror.org/01nzkaw91</idno>
          <orgName>Centre Inria d'Université Côte d'Azur</orgName>
          <desc>
            <address>
              <addrLine>2004 route des Lucioles BP 93 06902 Sophia Antipolis</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.inria.fr/centre/sophia/</ref>
          </desc>
          <listRelation>
            <relation active="#struct-300009" type="direct"/>
          </listRelation>
        </org>
        <org type="institution" xml:id="struct-300009" status="VALID">
          <idno type="ROR">https://ror.org/02kvxyf05</idno>
          <orgName>Institut National de Recherche en Informatique et en Automatique</orgName>
          <orgName type="acronym">Inria</orgName>
          <desc>
            <address>
              <addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine>
              <country key="FR"/>
            </address>
            <ref type="url">http://www.inria.fr/en/</ref>
          </desc>
        </org>
      </listOrg>
    </back>
  </text>
</TEI>