Improvements to Super-Peer Policy Communication Mechanisms

  • Paula Verghelet
  • Esteban MocskosEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 565)


The use of large distributed computing infrastructures has become a fundamental component in most of scientific and technological projects. Due to its highly distributed nature, one of the key topics to be addressed in large distributed systems (like Grids and Federation of Clouds) is the determination of the availability and state of resources. Having up-to-date information about resources in the system is extremely important as this is consumed by the scheduler for selecting the appropriate target in each job to be served.

The way in which this information is obtained and distributed is what is known as Resource Information Distribution Policy. A centralized organization presents several drawbacks, for example, a single point of failure. Notwithstanding, the static hierarchy has become the defacto implementation of grid information systems.

There is a growing interest in the interaction with the Peer to Peer (P2P) paradigm, pushing towards scalable solutions. Super Peer Policy (SP) is a decentralized policy which presents a notable improvement in terms of response time and expected number of results compared with decentralization one. While Hierarchical policy is valuable for small and medium-sized Grids, SP is more effective in very large systems and therefore is more scalable.

In this work, we analyze SP focusing on the communication between super-peers. An improvement to the standard protocol is proposed which leads to two new SP policies outperforming the standard implementation: N-SP and A2A-SP. These policies are analyzed in terms of obtained performance in Exponential and Barabási network topologies, network consumption and scalability.



E.M. is researcher of the CONICET. This work was partially supported by grants from Universidad de Buenos Aires (UBACyT 20020130200096BA) and CONICET (PIP 11220110100379).


  1. 1.
    Albert, R., Jeong, H., Barabási, A.L.: Internet: diameter of the world-wide web. Nature 401, 130–131 (1999). CrossRefGoogle Scholar
  2. 2.
    Assunção, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A., Buyya, R.: Big data computing and clouds: trends and future directions. J. Parallel Distrib. Comput. 79, 3–15 (2014)., Special issue on Scalable Systems for Big Data Management and AnalyticsGoogle Scholar
  3. 3.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Casanova, H., Legrand, A., Quinson, M.: SimGrid: a generic framework for large-scale distributed experiments. In: 10th IEEE International Conference on Computer Modeling and Simulation, pp. 126–131. IEEE Computer Society, Los Alamitos, March 2008Google Scholar
  5. 5.
    Cesario, E., Mastroianni, C., Talia, D.: Distributed volunteer computing for solving ensemble learning problems. Future Gener. Comput. Syst. (2015, in press).
  6. 6.
    Ergu, D., Kou, G., Peng, Y., Shi, Y., Shi, Y.: The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment. J. Supercomput. 64(3), 835–848 (2013)CrossRefGoogle Scholar
  7. 7.
    Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: 2008 Grid Computing Environments Workshop, GCE 2008, pp. 1–10, November 2008Google Scholar
  8. 8.
    Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. The Morgan Kaufmann Series in Computer Architecture and Design. Morgan Kaufmann Publishers Inc., San Francisco (2003)Google Scholar
  9. 9.
    Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: enabling scalable virtual organizations. Int. J. High Perform. Comput. Appl. 15(3), 200–222 (2001). CrossRefGoogle Scholar
  10. 10.
    Ghafarian, T., Deldari, H., Javadi, B., Yaghmaee, M.H., Buyya, R.: Cycloidgrid: a proximity-aware P2P-based resource discovery architecture in volunteer computing systems. J. Future Gener. Comput. Syst. 29(6), 1583–1595 (2013)., Including Special sections: High Performance Computing in the Cloud & Resource Discovery Mechanisms for P2P SystemsCrossRefGoogle Scholar
  11. 11.
    Iamnitchi, A., Foster, I., Nurmi, D.: A peer-to-peer approach to resource discovery in grid environments. In: Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing HPDC-11 (HPDC 2002), p. 419. IEEE, Edinbourgh, July 2002Google Scholar
  12. 12.
    Iamnitchi, A., Foster, I.: A peer-to-peer approach to resource location in Grid environments. In: Grid Resource Management: State of the Art and Future Trends, pp. 413–429. Kluwer Academic Publishers, Norwell (2004)Google Scholar
  13. 13.
    Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Liu, W., Nishio, T., Shinkuma, R., Takahashi, T.: Adaptive resource discovery in mobile cloud computing. Comput. Commun. 50, 119–129 (2014)., Green NetworkingCrossRefGoogle Scholar
  15. 15.
    Márquez, D.G., Mocskos, E.E., Slezak, D.F., Turjanski, P.G.: Simulation of resource monitoring and discovery in grids. In: Proceedings of HPC 2010 High-Performance Computing Symposium, pp. 3258–3270 (2010).
  16. 16.
    Mastroianni, C., Talia, D., Verta, O.: A super-peer model for resource discovery services in large-scale Grids. Future Gener. Comput. Syst. 21(8), 1235–1248 (2005). CrossRefGoogle Scholar
  17. 17.
    Mastroianni, C., Talia, D., Verta, O.: Designing an information system for Grids: comparing hierarchical, decentralized P2P and super-peer models. Parallel Comput. 34(10), 593–611 (2008)CrossRefGoogle Scholar
  18. 18.
    Mattmann, C., Garcia, J., Krka, I., Popescu, D., Medvidovic, N.: Revisiting the anatomy and physiology of the grid. J. Grid Comput. 13(1), 19–34 (2015)CrossRefGoogle Scholar
  19. 19.
    Mocskos, E.E., Yabo, P., Turjanski, P.G., Fernandez Slezak, D.: Grid matrix: a grid simulation tool to focus on the propagation of resource and monitoring information. Simul-T Soc. Mod. Sim. 88(10), 1233–1246 (2012)Google Scholar
  20. 20.
    Pipan, G.: Use of the TRIPOD overlay network for resource discovery. Future Gener. Comput. Syst. 26(8), 1257–1270 (2010). CrossRefGoogle Scholar
  21. 21.
    Plale, B., Jacobs, C., Jensen, S., Liu, Y., Moad, C., Parab, R., Vaidya, P.: Understanding Grid resource information management through a synthetic database benchmark/workload. In: CCGRID 2004: Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid, pp. 277–284. IEEE Computer Society, Washington, April 2004Google Scholar
  22. 22.
    Puppin, D., Moncelli, S., Baraglia, R., Tonellotto, N., Silvestri, F.: A grid information service based on peer-to-peer. In: Cunha, J.C., Medeiros, P.D. (eds.) Euro-Par 2005. LNCS, vol. 3648, pp. 454–464. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  23. 23.
    Ranjan, R., Harwood, A., Buyya, R.: Peer-to-peer-based resource discovery in global grids: a tutorial. IEEE Commun. Surv. Tut. 10(2), 6–33 (2008)CrossRefGoogle Scholar
  24. 24.
    Ranjan, R., Zhao, L.: Peer-to-peer service provisioning in cloud computing environments. J Supercomput. 65(1), 154–184 (2013)CrossRefGoogle Scholar
  25. 25.
    Ripeanu, M.: Peer-to-peer architecture case study: Gnutella network. In: 2001 Proceedings of the First International Conference on Peer-to-Peer Computing, pp. 99–100, August 2001Google Scholar
  26. 26.
    Shiers, J.: The worldwide LHC computing grid (worldwide LCG). Comput. Phys. Commun. 177(1–2), 219–223 (2007)CrossRefGoogle Scholar
  27. 27.
    Trunfio, P., Talia, D., Papadakis, C., Fragopoulou, P., Mordacchini, M., Pennanen, M., Popov, K., Vlassov, V., Haridi, S.: Peer-to-Peer resource discovery in Grids: models and systems. Future Gener. Comput. Syst. 23(7), 864–878 (2007)CrossRefGoogle Scholar
  28. 28.
    Williams, D.N., Drach, R., Ananthakrishnan, R., Foster, I., Fraser, D., Siebenlist, F., Bernholdt, D., Chen, M., Schwidder, J., Bharathi, S., et al.: The earth system grid: enabling access to multimodel climate simulation data. Bull. Am. Meteorol. Soc. 90(2), 195–205 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Laboratorio de Sistemas Complejos, Departamento de Computación, Facultad de Ciencias Exactas y NaturalesUniversidad de Buenos AiresBuenos AiresArgentina
  2. 2.Centro de Simulación Computacional p/Aplic. Tecnológicas/CSC-CONICETBuenos AiresArgentina

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