IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v196y2009i2p423-433.html
   My bibliography  Save this article

Optimization of a peer-to-peer system for efficient content replication

Author

Listed:
  • Cervellera, Cristiano
  • Caviglione, Luca

Abstract

This paper introduces a framework for the optimization of a peer-to-peer (p2p) based content replication system, aiming at actively exploiting the presence of a centralized component that represents a recent trend in content delivery architectures. To this purpose, we formalize a real-time mixed-integer nonlinear programming problem over a discrete time dynamic system, and propose a hybrid random/nonlinear programming scheme that allows to find good solutions while remaining computationally feasible. Two performance indexes, representing different objectives of the content replication process (e.g., speed vs. improved resistance against node failures), are discussed. Simulative tests are presented to prove the effectiveness of the proposed solution, with respect to typical strategies adopted by existing systems.

Suggested Citation

  • Cervellera, Cristiano & Caviglione, Luca, 2009. "Optimization of a peer-to-peer system for efficient content replication," European Journal of Operational Research, Elsevier, vol. 196(2), pages 423-433, July.
    • Handle: RePEc:eee:ejores:v:196:y:2009:i:2:p:423-433
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(08)00353-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dimitri P. Bertsekas & John N. Tsitsiklis, 1991. "An Analysis of Stochastic Shortest Path Problems," Mathematics of Operations Research, INFORMS, vol. 16(3), pages 580-595, August.
    2. Cervellera, Cristiano & Chen, Victoria C.P. & Wen, Aihong, 2006. "Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization," European Journal of Operational Research, Elsevier, vol. 171(3), pages 1139-1151, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. E. Nikolova & N. E. Stier-Moses, 2014. "A Mean-Risk Model for the Traffic Assignment Problem with Stochastic Travel Times," Operations Research, INFORMS, vol. 62(2), pages 366-382, April.
    2. Carey E. Priebe & Donniell E. Fishkind & Lowell Abrams & Christine D. Piatko, 2005. "Random disambiguation paths for traversing a mapped hazard field," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(3), pages 285-292, April.
    3. Dimitri P. Bertsekas, 2019. "Robust shortest path planning and semicontractive dynamic programming," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(1), pages 15-37, February.
    4. Pretolani, Daniele, 2000. "A directed hypergraph model for random time dependent shortest paths," European Journal of Operational Research, Elsevier, vol. 123(2), pages 315-324, June.
    5. Azadian, Farshid & Murat, Alper E. & Chinnam, Ratna Babu, 2012. "Dynamic routing of time-sensitive air cargo using real-time information," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 355-372.
    6. Emin Karagözoglu & Cagri Saglam & Agah R. Turan, 2020. "Tullock Brings Perseverance and Suspense to Tug-of-War," CESifo Working Paper Series 8103, CESifo.
    7. Huizhen Yu & Dimitri Bertsekas, 2013. "Q-learning and policy iteration algorithms for stochastic shortest path problems," Annals of Operations Research, Springer, vol. 208(1), pages 95-132, September.
    8. Arthur Flajolet & Sébastien Blandin & Patrick Jaillet, 2018. "Robust Adaptive Routing Under Uncertainty," Operations Research, INFORMS, vol. 66(1), pages 210-229, January.
    9. Benkert, Jean-Michel & Letina, Igor & Nöldeke, Georg, 2018. "Optimal search from multiple distributions with infinite horizon," Economics Letters, Elsevier, vol. 164(C), pages 15-18.
    10. Blai Bonet, 2007. "On the Speed of Convergence of Value Iteration on Stochastic Shortest-Path Problems," Mathematics of Operations Research, INFORMS, vol. 32(2), pages 365-373, May.
    11. James L. Bander & Chelsea C. White, 2002. "A Heuristic Search Approach for a Nonstationary Stochastic Shortest Path Problem with Terminal Cost," Transportation Science, INFORMS, vol. 36(2), pages 218-230, May.
    12. Borodin, Valeria & Bourtembourg, Jean & Hnaien, Faicel & Labadie, Nacima, 2015. "A multi-step rolled forward chance-constrained model and a proactive dynamic approach for the wheat crop quality control problem," European Journal of Operational Research, Elsevier, vol. 246(2), pages 631-640.
    13. Huiyuan Fan & Prashant K. Tarun & Victoria C. P. Chen & Dachuan T. Shih & Jay M. Rosenberger & Seoung Bum Kim & Robert A. Horton, 2018. "Data-driven optimization for Dallas Fort Worth International Airport deicing activities," Annals of Operations Research, Springer, vol. 263(1), pages 361-384, April.
    14. Matsubayashi, Nobuo & Nishino, Hisakazu, 1999. "An application of Lemke's method to a class of Markov decision problems," European Journal of Operational Research, Elsevier, vol. 116(3), pages 584-590, August.
    15. D. Fu & Y. Li & G. Huang, 2013. "A Factorial-based Dynamic Analysis Method for Reservoir Operation Under Fuzzy-stochastic Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(13), pages 4591-4610, October.
    16. Gauvin, Charles & Delage, Erick & Gendreau, Michel, 2018. "A stochastic program with time series and affine decision rules for the reservoir management problem," European Journal of Operational Research, Elsevier, vol. 267(2), pages 716-732.
    17. Özlem Çavuş & Andrzej Ruszczyński, 2014. "Computational Methods for Risk-Averse Undiscounted Transient Markov Models," Operations Research, INFORMS, vol. 62(2), pages 401-417, April.
    18. Huizhen Yu & Dimitri P. Bertsekas, 2013. "On Boundedness of Q-Learning Iterates for Stochastic Shortest Path Problems," Mathematics of Operations Research, INFORMS, vol. 38(2), pages 209-227, May.
    19. Guillot, Matthieu & Stauffer, Gautier, 2020. "The Stochastic Shortest Path Problem: A polyhedral combinatorics perspective," European Journal of Operational Research, Elsevier, vol. 285(1), pages 148-158.
    20. Karagözoğlu, Emin & Sağlam, Çağrı & Turan, Agah R., 2021. "Perseverance and suspense in tug-of-war," Journal of Mathematical Economics, Elsevier, vol. 95(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:196:y:2009:i:2:p:423-433. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.