PSO vs. ACO, Data Grid Replication Services Performance Evaluation

  • Víctor Méndez
  • Felix García Carballeira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4331)


Data Grid replication is critical for improving data intensive applications performance, providing fault tolerance and load balancing. Most of the techniques for data replication use Giggle as a framework for Replica Location Services (RLS), combined with other services for replica selection and optimization. Our previous work have proposed an enhanced Giggle framework, that simplify the location service using a flat catalogue structure, that combined with appropriate heuristic, obtain much better performances than traditional approaches. With this aim, we propose the use of Emergent Artificial Intelligence (EAI) techniques on data replication: Particle Swarm Optimisation(PSO) and Ant Colony Optimisation(ACO). This paper contribution is an experiment comparison between PSO, ACO, a canonical replication algorithm and other state of the art economic model replication algorithm. The experiments are design on two different network topologies. The simulation results confirm that PSO and ACO using the enhanced Giggle, improve performance over traditional solutions.


Market Model Data Replication Simulation Series Pheromone Concentration Grid Site 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Foster, I., Kesselman, C., Nick, J.M., Tuecke, S.: The physiology of the grid an open grid services architecture for distributed system integration. Technical report, Globus Proyect Draft Overwiev Paper (2002)Google Scholar
  2. 2.
    Chervenak, A.L., Deelman, E., Foster, I., Iamnitchi, A., Kesselman, C., Hoschek, W., Kunszt, P., Ripeanu, M., Schwartzkopf, B., Stockinger, H., Stockinger, K., Tierney, B.: Giggle: A framework for constructing scalable replica location services. In: Proc. of the IEEE Supercomputing Conference (SC 2002), November 2002, IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  3. 3.
    Méndez, V., García, F.: Pso-lru algorithm for datagrid replication service. In: Proceedings of 2006 International Workshop on High-Performance Data Management in Grid Environments, Springer, Heidelberg (2006)Google Scholar
  4. 4.
    Foster, I., Kesselman, C.: Globus: A metacomputing infrastructure toolkit. IJSA 11, 115–128 (1997)Google Scholar
  5. 5.
    Cameron, D.G., Carvajal-Schiaffino, R., Millar, A.P., Nicholson, C., Stockinger, K., Zini, F.: Analysis of scheduling and replica optimisation strategies for data grids using optorsim. International Journal of Grid Computing 2, 57–69 (2004)CrossRefGoogle Scholar
  6. 6.
    Méndez, V., García, F.: Ant colony optimization for datagrid replication services. Technical report, RR-06-08. Computer Science Departament, Universidad de Zaragoza (2006)Google Scholar
  7. 7.
    Capozza, L., Stockinger, K., Zini, F.: Preliminary evaluation of revenue prediction functions for economically-effective file replication. Technical report, DataGrid-02-TED-020724, Geneva, Switzerland (July 2002)Google Scholar
  8. 8.
    Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE Press, Los Alamitos (1998)Google Scholar
  9. 9.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: An autocatalytic optimizing process. Technical report no. 91-016 revised. Technical report, Politecnico di Milano (1991)Google Scholar
  10. 10.
    Dorigo, M.: Optimization, learning and natural algorithms. PhD thesis, Politecnico di Milano, Italy (1992)Google Scholar
  11. 11.
    Leijen, D.: Parsec, a fast combinator parser. Technical report, Computer Science Department, University of Utrecht (2002)Google Scholar
  12. 12.
    Granger, R., Worthington, G.L., Patt, B.N., Y. (eds.): The DiskSim Simulation Environment. Version 2.0 Reference Manual. University of Michigan (1999)Google Scholar
  13. 13.
    Bell, W.H., Cameron, D.G., Capozza, L., Millar, A.P., Stockinger, K., Zini, F.: Optorsim - a grid simulator for studying dynamic data replication strategies. International Journal of High Performance Computing Applications 17 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Víctor Méndez
    • 1
  • Felix García Carballeira
    • 2
  1. 1.Universidad de Zaragoza, CPS, Edificio Ada Byron, Universidad de Zaragoza, CPSZaragozaSpain
  2. 2.Universidad Carlos III de Madrid, EPSLeganés, MadridSpain

Personalised recommendations