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D6.1 PIACERE run-time monitoring and self-learning, self-healing platform - v1_V1.0

TECNALIA


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  <identifier identifierType="DOI">10.5281/zenodo.6821765</identifier>
  <creators>
    <creator>
      <creatorName>TECNALIA</creatorName>
    </creator>
  </creators>
  <titles>
    <title>D6.1 PIACERE run-time monitoring and self-learning, self-healing platform - v1_V1.0</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>Monitoring, Forecast, Healing, Security, Availability, Performance</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-07-12</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Report"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/6821765</alternateIdentifier>
  </alternateIdentifiers>
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  <version>1.0</version>
  <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;These deliverables will contain the main outcomes from M1 to M30 of T6.1-T6.4 due to the high dependency of all the different tasks. It will include the monitoring stack coming from task 6.1 with all the time series data collected as well as the monitoring from the security policies from task 6.4, the set of machine learning algorithms (task 6.2) that comprise the self-learning mechanisms and the self-healing strategies (task 6.3) that trigger an optimized redeployment (see WP5). It will be an iterative process. Each deliverable will comprise a Technical Specification Report.&lt;/p&gt;</description>
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