Report Open Access

Machine Learning applications on OpenStack log data analysis

Ravi Charan Nudurupati

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  <identifier identifierType="DOI">10.5281/zenodo.3885380</identifier>
      <creatorName>Ravi Charan Nudurupati</creatorName>
    <title>Machine Learning applications on OpenStack log data analysis</title>
    <subject>CERN openlab</subject>
    <subject>Summer Student Programme</subject>
    <date dateType="Issued">2020-06-08</date>
  <resourceType resourceTypeGeneral="Text">Report</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3885379</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;A massive amount of data is generated by the Openstack cloud services in the format of service logs. Besides timestamps and log level fields, these logs contain additional information useful for pattern analysis. Unfortunately, this information is generally exposed in semi-structured text format, not allowing direct analysis without additional munging of the data. Traditional approaches to extract information from those fields are rule-based, mainly applying regular expressions upon knowledge of the text structure. These approaches require a pre-knowledge of all text patterns and are not scalable with the growth of the services. This report&amp;nbsp;proposes a solution that is a mixture of the MinHash Locality Sensitive Hashing and the DB scan algorithm for data clustering.&amp;nbsp;&lt;br&gt;
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