• DocumentCode
    633091
  • Title

    Storage Mining: Where IT Management Meets Big Data Analytics

  • Author

    Yang Song ; Alatorre, Gabriel ; Mandagere, Nagapramod ; Singh, Ashutosh

  • Author_Institution
    IBM Almaden Res. Center, San Jose, CA, USA
  • fYear
    2013
  • fDate
    June 27 2013-July 2 2013
  • Firstpage
    421
  • Lastpage
    422
  • Abstract
    The emerging paradigm shift to cloud based data center infrastructures imposes remarkable challenges to IT management operations, e.g., due to virtualization techniques and more stringent requirements for cost and efficiency. On one hand, the voluminous data generated by daily IT operations such as logs and performance measurements contain abundant information and insights which can be leveraged to assist the IT management. On the other hand, traditional IT management solutions cannot consume and exploit the rich information contained in the data due to the daunting volume, velocity, variety, as well as the lack of scalable data mining and machine learning frameworks to extract insights from such raw data. In this paper, we present our on-going research thrust of designing novel IT management solutions by leveraging big data analytics frameworks. As an example, we introduce our project of Storage Mining, which exploits big data analytics techniques to facilitate storage cloud management. The challenges are discussed and our proof-of-concept big data analytics framework is presented.
  • Keywords
    cloud computing; computer centres; data analysis; data mining; storage management; IT management operations; IT management solutions; big data analytics framework; cloud based data center infrastructures; storage cloud management; storage mining; Analytical models; Data handling; Data mining; Data storage systems; Information management; Predictive models; Training; Big Data Analytics; IT Management;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2013 IEEE International Congress on
  • Conference_Location
    Santa Clara, CA
  • Print_ISBN
    978-0-7695-5006-0
  • Type

    conf

  • DOI
    10.1109/BigData.Congress.2013.66
  • Filename
    6597170