Skip to main content
Springer Nature Link
Log in

Efficient P2P Inspired Policy to Distribute Resource Information in Large Distributed Systems

  • Conference paper
  • First Online:
High Performance Computing (CARLA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 697))

Included in the following conference series:

Abstract

The computational infrastructures are becoming larger and more complex. Their organization and interconnection are acquiring new dimensions with the increasing adoption of Cloud Technology and the establishment of Federations of cloud providers.

These large interconnected systems require monitoring at different levels of the infrastructure: from the availability of hardware resources to the effective provision of services and verification of terms of the established agreements.

Monitoring becomes a fundamental component of any Cloud Service or Federation, as the up-to-date information about resources in the system is extremely important to be used as an input to the scheduler component. The way in which the different members of such a distributed system obtain and distribute the resource information is what is known as Resource Information Distribution Policy.

Moving towards the obtention of a scalable and easy to maintain policy leads to interaction with the Peer to Peer (P2P) paradigm. Some of the proposed policies are based on establishing a ranking according to previous communications between nodes. These policies are known as learning based methods or Best-Neighbor (BN). However, the use of this type of policies shows poor performance and limited scalability compared with defacto Hierarchical or other hybrid policies.

In this work, we introduce pBN which is a fully distributed resource information policy based on P2P. We analyze some reasons that could produce the poor performance in standard BN and propose an improvement which shows performance and bandwidth consumption similar to Hierarchical policy and other hybrid variations. To compare the different policies, a specific simulation tool is used with different system sizes and exponential network topology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Agrawal, D., Giles, J., Lee, K.W., Voruganti, K., Filali-Adib, K.: Policy-based validation of san configuration. In: Proceedings of Fifth IEEE International Workshop on Policies for Distributed Systems and Networks, POLICY 2004, pp. 77–86, June 2004

    Google Scholar 

  2. Albert, R., Jeong, H., Barabási, A.L.: Internet: diameter of the world-wide web. Nature 401, 130–131 (1999). http://adsabs.harvard.edu/abs/1999Natur.401.130A

    Article  Google Scholar 

  3. 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. 79–80, 3–15 (2014). http://www.sciencedirect.com/science/article/pii/S0743731514001452, special Issue on Scalable Systems for Big Data Management and Analytics

    Google Scholar 

  4. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks. In: Proceedings of AAAI Conference on Weblogs and Social Media, May 2009. http://www.aaai.org/ocs/index.php/ICWSM/09/paper/view/154

  6. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008). http://stacks.iop.org/1742-5468/2008/i=10/a=P10008

    Article  Google Scholar 

  7. 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 2008

    Google Scholar 

  8. Cesario, E., Mastroianni, C., Talia, D.: Distributed volunteer computing for solving ensemble learning problems. Future Gen. Comput. Syst. (2015, in press). http://www.sciencedirect.com/science/article/pii/S0167739X15002332

  9. Clayman, S., Toffetti, G., Galis, A., Chapman, C.: Monitoring services in a federated cloud: the RESERVOIR experience. In: Achieving Federated and Self-Manageable Cloud Infrastructures: Theory and Practice, pp. 242–265. IGI Global, May 2012

    Google Scholar 

  10. 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). http://dx.doi.org/10.1007/s11227-011-0625-1

    Article  Google Scholar 

  11. Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: Grid Computing Environments Workshop, GCE 2008, pp. 1–10, November 2008

    Google Scholar 

  12. 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 

  13. 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). http://portal.acm.org/citation.cfm?id=1080667

    Article  Google Scholar 

  14. Ghafarian, T., Deldari, H., Javadi, B., Yaghmaee, M.H., Buyya, R.: Cycloidgrid: a proximity-aware P2P-based resource discovery architecture in volunteer computing systems. Future Gen. Comput. Syst. 29(6), 1583–1595 (2013). Including Special sections: High Performance Computing in the Cloud & Resource Discovery Mechanisms for P2P Systems. http://www.sciencedirect.com/science/article/pii/S0167739X12001665

    Article  Google Scholar 

  15. Hasanzadeh, M., Meybodi, M.R.: Distributed optimization grid resource discovery. J. Supercomput. 71(1), 87–120 (2015)

    Article  MATH  Google Scholar 

  16. 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 2002

    Google Scholar 

  17. Iamnitchi, A., Foster, I.: A peer-to-peer approach to resource location in grid environments. In: Nabrzyski, J., Schopf, J.M., Weglarz, J. (eds.) Grid Resource Management: State of the Art and Future Trends, pp. 413–429. Kluwer Academic Publishers, Norwell (2004)

    Chapter  Google Scholar 

  18. Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  19. Kertesz, A., Kecskemeti, G., Oriol, M., Kotcauer, P., Acs, S., Rodríguez, M., Mercè, O., Marosi, A.C., Marco, J., Franch, X.: Enhancing federated cloud management with an integrated service monitoring approach. J. Grid Comput. 11(4), 699–720 (2013)

    Article  Google Scholar 

  20. Liu, W., Nishio, T., Shinkuma, R., Takahashi, T.: Adaptive resource discovery in mobile cloud computing. Comput. Commun. 50, 119–129 (2014). Green Networking. http://www.sciencedirect.com/science/article/pii/S0140366414000590

    Article  Google Scholar 

  21. Mastroianni, C., Talia, D., Verta, O.: A super-peer model for resource discovery services in large-scale grids. Future Gen. Comput. Syst. 21(8), 1235–1248 (2005). http://www.sciencedirect.com/science/article/pii/S0167739X05000701

    Article  Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. 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)

    Article  Google Scholar 

  24. Meshkova, E., Riihijärvi, J., Petrova, M., Mähönen, P.: A survey on resource discovery mechanisms, peer-to-peer and service discovery frameworks. Comput. Netw. 52(11), 2097–2128 (2008). http://www.sciencedirect.com/science/article/pii/S138912860800100X

    Article  Google Scholar 

  25. 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 

  26. Olaifa, M., Mapayi, T., Merwe, R.V.D.: Multi ant LA: an adaptive multi agent resource discovery for peer to peer grid systems. In: Science and Information Conference (SAI), pp. 447–451, July 2015

    Google Scholar 

  27. Pipan, G.: Use of the TRIPOD overlay network for resource discovery. Future Gen. Comput. Syst. 26(8), 1257–1270 (2010). http://www.sciencedirect.com/science/article/pii/S0167739X1000018X

    Article  Google Scholar 

  28. 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: Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid, CCGRID 2004, pp. 277–284. IEEE Computer Society, Washington, April 2004

    Google Scholar 

  29. 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). doi:10.1007/11549468_52

    Chapter  Google Scholar 

  30. Ranjan, R., Harwood, A., Buyya, R.: Peer-to-peer-based resource discovery in global grids: a tutorial. IEEE Commun. Surv. Tutor. 10(2), 6–33 (2008)

    Article  Google Scholar 

  31. Ranjan, R., Zhao, L.: Peer-to-peer service provisioning in cloud computing environments. J. Supercomput. 65(1), 154–184 (2013)

    Article  Google Scholar 

  32. Ripeanu, M.: Peer-to-peer architecture case study: Gnutella network. In: Proceedings of First International Conference on Peer-to-Peer Computing, pp. 99–100, August 2001

    Google Scholar 

  33. Shiers, J.: The worldwide LHC computing grid (worldwide LCG). Comput. Phys. Commun. 177(1–2), 219–223 (2007)

    Article  Google Scholar 

  34. 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 Gen. Comput. Syst. 23(7), 864–878 (2007)

    Article  Google Scholar 

  35. Verghelet, P., Mocskos, E.: Improvements to super-peer policy communication mechanisms. In: Osthoff, C., Navaux, P.O.A., Barrios Hernandez, C.J., Silva Dias, P.L. (eds.) CARLA 2015. CCIS, vol. 565, pp. 73–86. Springer, Cham (2015). doi:10.1007/978-3-319-26928-3_6

    Google Scholar 

  36. Verghelet, P., Slezak, D.F., Turjanski, P., Mocskos, E.: Using distributed local information to improve global performance in grids. CLEIej 15(3), 8 (2012). http://www.clei.cl/cleiej/papers/v15i3p7.pdf

    Google Scholar 

  37. 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)

    Article  Google Scholar 

Download references

Acknowledgments

E.M. is researcher at the CONICET. This work was partially supported by grants from Universidad de Buenos Aires (UBACyT 20020130200096BA), CONICET (PIP 11220110100379 and PIO 13320150100020CO), and ANPCyT (PICT-2015-2761 and PICT-2015-0370).

Author information

Authors and Affiliations

  1. Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, C1428EGA, Buenos Aires, Argentina

    Paula Verghelet & Esteban Mocskos

  2. Centro de Simulación Computacional p/Aplic. Tecnológicas/CSC-CONICET, Godoy Cruz 2390, C1425FQD, Buenos Aires, Argentina

    Esteban Mocskos

Authors
  1. Paula Verghelet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Esteban Mocskos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Esteban Mocskos .

Editor information

Editors and Affiliations

  1. Universidad Industrial de Santander, Bucaramanga, Colombia

    Carlos Jaime Barrios Hernández

  2. Centro de Investigación y de Estudios Avanzados, CINVESTAV-IPN, Ciudad de México, Mexico

    Isidoro Gitler

  3. Instituto Nacional de Investigaciones Nucleares, La Marquesa, Estado de México, Mexico

    Jaime Klapp

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Verghelet, P., Mocskos, E. (2017). Efficient P2P Inspired Policy to Distribute Resource Information in Large Distributed Systems. In: Barrios Hernández, C., Gitler, I., Klapp, J. (eds) High Performance Computing. CARLA 2016. Communications in Computer and Information Science, vol 697. Springer, Cham. https://doi.org/10.1007/978-3-319-57972-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57972-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57971-9

  • Online ISBN: 978-3-319-57972-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics