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Self-adaptive Architectures for Autonomic Computational Science

  • Shantenu Jha
  • Manish Parashar
  • Omer Rana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6090)

Abstract

Self-adaptation enables a system to modify it’s behaviour based on changes in its operating environment. Such a system must utilize monitoring information to determine how to respond either through a systems administrator or automatically (based on policies pre-defined by an administrator) to such changes. In computational science applications that utilize distributed infrastructure (such as Computational Grids and Clouds), dealing with heterogeneity and scale of the underlying infrastructure remains a challenge. Many applications that do adapt to changes in underlying operating environments often utilize ad hoc, application-specific approaches. The aim of this work is to generalize from existing examples, and thereby lay the foundation for a framework for Autonomic Computational Science (ACS). We use two existing applications – Ensemble Kalman Filtering and Coupled Fusion Simulation – to describe a conceptual framework for ACS, consisting of mechanisms, strategies and objectives, and demonstrate how these concepts can be used to more effectively realize pre-defined application objectives.

Keywords

History Match Autonomic Computing Execution Unit Conceptual Architecture Tuning Mechanism 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shantenu Jha
    • 1
    • 2
  • Manish Parashar
    • 3
  • Omer Rana
    • 4
  1. 1.Center for Computation & Technology and Department of Computer ScienceLouisiana State UniversityUSA
  2. 2.e-Science InstituteUniversity of EdinburghUK
  3. 3.Department of Electrical & Computer EngineeringRutgers UniversityUSA
  4. 4.School of Computer ScienceCardiff UniversityUK

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