Evaluating Tools for Performance Modeling of Grid Applications

  • Mariela Curiel
  • Gustavo Alvarez
  • Leonardo Flores
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4331)


A Grid is a collection of heterogeneous distributed computing resources for solving large-scale computational and data intensive problems. It is a dynamic environment where resources attributes -such as load- change constantly hindering performance evaluation activities. Performance models could be a solution to this problem because they provide a way of performing repeatable and controllable experiments. Several tools have been developed for modeling scheduling algorithms in Grids. We believe, however, that if these tools are to be used for modeling application performance they should be improved by adding some particular features. In this paper, we identify such features and evaluate two modeling tools based on those features. These tools are used to represent the execution of applications in the Grid SUMA.


Parallel Application Grid Application Asynchronous Message Execution Agent SUMA Component 
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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  1. 1.
    Franks, G.: Performance Analysis of Distributed Server Systems. PhD thesis, Carleton University (2000)Google Scholar
  2. 2.
    Mascarenhas, E.: A System for Multithreaded Parallel Simulation with Migrant Thread and Objects. PhD thesis, Purdue University (1996)Google Scholar
  3. 3.
    Sulistio, A., Poduval, G., Buyya, R., Tham, C.: Constructing a grid simulation with differentiated network service using gridsim. In: Proc. of the 6th. International Conference on Internet Computing (ICOMP 2005) (2005)Google Scholar
  4. 4.
    Cardinale, Y., Curiel, M., Figueira, C., García, P., Hernández, E.: Implementation of a corba-based metacomputing system. In: Proc. of Workshop on Java for High Performance Computing. LNCS (2001)Google Scholar
  5. 5.
    Snavely, A., Chun, G., Casanova, H., der Wijngaart, R.V., Frumkin, M.: Benchmarks for grid computing: A review of ongoing efforts and fututre directions. Sigmetrics Perfor. Eval. Rev. 30(4), 27–32 (2003)CrossRefGoogle Scholar
  6. 6.
    Quétier, B., Capello, F.: A survey of grid research tools: simulators, emulators and real life platforms. In: Proc. of 17th IMACS World Congress (IMAC 2005), France (2005)Google Scholar
  7. 7.
    Petriu, D.C., Woodside, C.: Software performance models from systems scenarios in use case maps. In: Proc. of TOOLS. LNCS, vol. 794, pp. 159–177. Springer, Heidelberg (2002)Google Scholar
  8. 8.
    Sulistio, A., Cibej, U., Robic, B., Buyya, R.: A Toolkit for Modeling and Simulation of Data Grids with Integration of Data Storage, Replication and Analysis. Technical Report GRIS-TR-2005-13, University of Melbourne (2005)Google Scholar
  9. 9.
    Sulistio, A., Yeo, C.S., Buyya, R.: Visual modeler for grid modeling and simulation (gridsim) toolkit. In: Proc. of the 3rd. International Conference on Computational Science (ICCS 2003). LCNS, Springer, Heidelberg (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mariela Curiel
    • 1
  • Gustavo Alvarez
    • 1
  • Leonardo Flores
    • 1
  1. 1.Departamento de Computación y Tecnología de la InformaciónUniversidad Simón BolívarCaracasVenezuela

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