Conference paper Open Access

SheerMP: Optimized Streaming Analytics-as-a-Service over Multi-site Multi-platform Settings

George Stamatakis; Antonios Kontaxakis; Alkis Simitsis; Nikos Giatrakos; Antonios Deligiannakis


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  <identifier identifierType="DOI">10.5281/zenodo.6345357</identifier>
  <creators>
    <creator>
      <creatorName>George Stamatakis</creatorName>
      <affiliation>Athena Research Center</affiliation>
    </creator>
    <creator>
      <creatorName>Antonios Kontaxakis</creatorName>
      <affiliation>Universite Libre de Bruxelles &amp; Athena Research Center</affiliation>
    </creator>
    <creator>
      <creatorName>Alkis Simitsis</creatorName>
      <affiliation>Athena Research Center</affiliation>
    </creator>
    <creator>
      <creatorName>Nikos Giatrakos</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8218-707X</nameIdentifier>
      <affiliation>Athena Research Center &amp; Technical University of Crete</affiliation>
    </creator>
    <creator>
      <creatorName>Antonios Deligiannakis</creatorName>
      <affiliation>Athena Research Center &amp; Technical University of Crete</affiliation>
    </creator>
  </creators>
  <titles>
    <title>SheerMP: Optimized Streaming Analytics-as-a-Service over Multi-site Multi-platform Settings</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>Big Data</subject>
    <subject>Optimization</subject>
    <subject>Data Streams</subject>
    <subject>Resource Allocation</subject>
    <subject>Analytics-as-a-Service</subject>
    <subject>Software Architectures</subject>
    <subject>Apache Flink</subject>
    <subject>Apache Spark</subject>
    <subject>Apache Kafka</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-03-29</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/6345357</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.6345356</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/infore-project</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Analytics are in the core of many emerging applications and can greatly benefit from the abundance of data and the progress in the processing capabilities of modern hardware. Still, new challenges arise with the extreme complexity of deciding how to execute analytics workflows given the plethora of choices of various cloud providers, the fragmented nature of diverse Big Data technologies, and the difficult task of resource provisioning to dynamically satisfy the demands of running streaming analytics over time. In this paper, we demonstrate a prototype system that optimizes streaming analytics workflows across Big Data platforms and computer clusters. Our system is the first that (i) considers a multi-user setup, (ii) examines the availability of multiple (potentially, geo-dispersed) compute choices, and (iii) provides a holistic framework covering a wide variety of practical optimization and adaptive resource allocation scenarios over a variety of streaming Big Data platforms&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/825070/">825070</awardNumber>
      <awardTitle>Interactive Extreme-Scale Analytics and Forecasting</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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