Dynamic on Demand Virtual Clusters in Grid

  • Authors:

  • Mario Leandro Bertogna;Eduardo Grosclaude;Marcelo Naiouf;Armando Giusti;Emilio Luque

  • Affiliations:

  • Department of Computer Science, Universidad Nacional del Comahue, Buenos Aires, Argentina 1400;Department of Computer Science, Universidad Nacional del Comahue, Buenos Aires, Argentina 1400;Informatic Research Institute LIDI, Universidad Nacional de La Plata, Argentina;Informatic Research Institute LIDI, Universidad Nacional de La Plata, Argentina;Computer Architecture and Operating System Department, Universidad Autónoma de Barcelona, Spain

  • Venue:
  • Euro-Par 2008 Workshops - Parallel Processing
  • Year:
  • 2009

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Abstract

In Grid environments, many different resources are intended to work in a coordinated manner, each resource having its own features and complexity. As the number of resources grows, simplifying automation and management is among the most important issues to address. This paper's contribution lies on the extension and implementation of a grid metascheduler that dynamically discovers, creates and manages on-demand virtual clusters. The first module selects the clusters using graph heuristics. The algorithm then tries to find a solution by searching a set of clusters, mapped to the graph, that achieve the best performance for a given task. The second module, one per-grid node, monitors and manages physical and virtual machines. When a new task arrives, these modules modify virtual machine's configuration or use live migration to dynamically adapt resource distribution at the clusters, obtaining maximum utilization. Metascheduler components and local administrator modules work together to make decisions at run time to balance and optimize system throughput. This implementation results in performance improvement of 20% on the total computing time, with machines and clusters processing 100% of their working time. These results allow us to conclude that this solution is feasible to be implemented on Grid environments, where automation and self-management are key to attain effective resource usage.