GRID Resource Searching on the GridSim Simulator

  • Antonia Gallardo
  • Luis Díaz de Cerio
  • Roque Messeguer
  • Andreu Pere Isern-Deyà
  • Kana Sanjeevan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5544)


Nowadays, the Grid is the focus of multiple researches. Our work is centered on Resource Management for Grids as it is an opened and current research area. Decentralized, scalable and efficient resource search algorithms are one of the key issues for resource management in large Grid systems. Resource search is required in order to allocate applications and data efficiently and to maintain the quality of service at runtime, just to mention some examples. In this work, we propose a scheme that presents essential characteristics for self-configuring search and is able to handle dynamic resources, such as memory capacity. Our approach consists on a hypercube topology connecting the nodes and a scalable and self-configuring search procedure. The algorithm improves the probability of reaching the alive nodes in the Grid even in the presence of non-alive ones (inaccessible, crashed or heavy loaded nodes). In this paper, after the theory’s description, we present some results obtained by running our search protocol on the GridSim simulator. We have evaluated 6 different metrics performing several resources searches and we show the arithmetic media for each measure.


GridSim Hypercube Self-Configuring Search Algorithms 


  1. 1.
    Nabrzyski, J., Schopf, J.M., Weglarz, J.: Grid Resource Management. State of the Art and future Trends. Kluwer Publishing, Academic publishers (2004)Google Scholar
  2. 2.
    Foster, I., Iamnitchi, A.: On Death, Taxes, and the Convergence of Peer-to-Peer and Grid Computing. In: Kaashoek, M.F., Stoica, I. (eds.) IPTPS 2003. LNCS, vol. 2735. Springer, Heidelberg (2003)Google Scholar
  3. 3.
    Gummadi, K., Gummadi, R., Gribble, S., Ratnasamy, S., Shenker, S., Stoica, I.: The Impact of DHT Routing Geometry on Resilience and Proximity. Applications, Technologies, Architectures and Protocols for Computer Communications, 381–394 (2003)Google Scholar
  4. 4.
    Buyya, R., Murshed, M.: GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing. Concurrency and Computation: Practice and Experience 14(13-15), 1175–1220 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Liebeherr, J., Beam, T.K.: HyperCast: A Protocol for Maintaining Multicast Group Members in a Logical Hypercube Topology. In: Rizzo, L., Fdida, S. (eds.) NGC 1999. LNCS, vol. 1736, pp. 72–89. Springer, Heidelberg (1999)Google Scholar
  6. 6.
    The Globus Toolkit, (last access 15/02/2009)
  7. 7.
    Iamnitchi, A., Foster, I.: On fully decentralized resource discovery in grid environments. In: Lee, C.A. (ed.) Grid 2001. LNCS, vol. 2242, pp. 51–62. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Maymounkov, P., Mazières, D.: Kademlia: A Peer-to-Peer Information System Based on the XOR Metric. In: Druschel, P., Kaashoek, M.F., Rowstron, A. (eds.) IPTPS 2002. LNCS, vol. 2429, pp. 53–65. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Oppenheimer, D., Albrecht, J., Patterson, D., Vahdat, A.: Design and Implementation Tradeoffs for Wide-Area Resource Discovery. In: 14th IEEE Symposium on High Performance Distributed Computing, Research Triangle Park, NC USA (HPDC 2005) (2005)Google Scholar
  10. 10.
    March, V., Teo, Y.M., Wang, X.: DGRID A DHT-Based Resource Indexing and Discovery Scheme for Computational Grids. In: 5th Australasian Symposium on Grid Computing and e-Research (AusGrid 2007), pp. 41–48 (2007)Google Scholar
  11. 11.
    Ren, H., Wang, Z., Liu, Z.: A Hyper-cube based P2P Information Service for Data Grid. In: Conference on Grid and Cooperative Computing (GCC 2006), pp. 508–513 (2006)Google Scholar
  12. 12.
    Gallardo, A., Díaz de Cerio, L., Sanjeevan, K.: Scalable Self-Configuring Resource Discovery for Grids. In: V Brazilian Workshop on Grid Computing and Applications (WCGA 2007) (2007)Google Scholar
  13. 13.
    Gallardo, A., Díaz de Cerio, L., Sanjeevan, K., Bona, L.C.E.: HGRID: An Adaptive Grid Resource Discovery. In: 2nd International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2008) (2008)Google Scholar
  14. 14.
    Medina, A., Lakhina, A., Matta, I., Byers, J.: BRITE: Universal Topology Generator from a User’s Perspective, pp. 1–47 (2001), (last access 15/02/2009)
  15. 15.
    Buyya, R., Ranjan, R., Broberg, J., Dias de Assuncao, M.: GridSim: A Grid Simulation Toolkit for Resource Modelling and Application Scheduling for Parallel and Distributed Computing, (last access 15/02/2009)

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Antonia Gallardo
    • 1
  • Luis Díaz de Cerio
    • 2
  • Roque Messeguer
    • 1
  • Andreu Pere Isern-Deyà
    • 3
  • Kana Sanjeevan
    • 1
  1. 1.Departament de Arquitectura de ComputadorsUniversitat Politécnica de CatalunyaCastelldefelsSpain
  2. 2.Departamento de Automática y ComputaciónUniversidad Pública de NavarraPamplonaSpain
  3. 3.Departament de Matemàtiques i InformàticaUniversitat de les Illes BalearsPalma de MallorcaSpain

Personalised recommendations