• DocumentCode
    2497744
  • Title

    Workload Models for Autonomic Database Management Systems

  • Author

    Martin, Pat ; Elnaffar, Said ; Wasserman, Ted

  • Author_Institution
    Sch. of Comput., Queen´´s Univ., Kingston, Ont.
  • fYear
    2006
  • fDate
    16-18 July 2006
  • Firstpage
    10
  • Lastpage
    10
  • Abstract
    Autonomic computing is a promising approach to the problem of effectively managing large complex software systems such as database management systems (DBMSs). In order to be self-managing, an autonomic DBMS (ADBMS) must understand key aspects of its workload, including composition, frequency patterns, intensity and resource requirements. It must therefore use and maintain different characterizations, or models, of the workload to support its various kinds of decision-making. Our research into various aspects of ADBMSs has led us to develop a number of different workload models. In this paper, we examine the importance of workload models to ADBMSs. We discuss the types of workload models needed by ADBMSs and describe examples from our research. We then outline the requirements for an infrastructure to develop and maintain the workload models needed by an ADBMS
  • Keywords
    database management systems; fault tolerant computing; DBMS; autonomic computing; autonomic database management systems; workload models; Database systems; Decision making; Disaster management; Educational institutions; Frequency; Laboratories; Resource management; Silicon; Software systems; Unsupervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Autonomic and Autonomous Systems, 2006. ICAS '06. 2006 International Conference on
  • Conference_Location
    Silicon Valley, CA
  • Print_ISBN
    0-7695-2653-5
  • Type

    conf

  • DOI
    10.1109/ICAS.2006.64
  • Filename
    1690220