Towards Intelligent Management of Very Large Computing Systems

Conference paper


The increasing complexity of current and future very large computing systems with a rapidly growing number of cores and nodes requires high human effort on administration and maintenance of these systems. Existing monitoring tools are neither scalable nor capable to reduce the overwhelming flow of information and provide only essential information of high value. Current management tools lack on scalability and capability to process a huge amount of information intelligently by relating several data and information from various sources together for making right decisions on error/fault handling. In order to solve these problems, we present a solution designed within the TIMaCS project, a hierarchical, scalable, policy based monitoring and management framework.


Virtual Machine High Performance Computing Management Framework Physical Machine Mean Time Between Failure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The results presented in this paper are partially funded by Federal Ministry of Education and Research (BMBF) through the TIMaCS [6] project.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.High Performance Computing Center StuttgartStuttgartGermany
  2. 2.Zentrum für Informationsdienste und Hochleistungsrechnen (ZIH)Technische Universität DresdenDresdenGermany
  3. 3.Department of Mathematics and Computer ScienceUniversity of MarburgMarburgGermany
  4. + computing agTübingenGermany
  5. 5.NEC High Performance Computing EuropeStuttgartGermany

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