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Distributed and Parallel Databases

, Volume 28, Issue 2–3, pp 187–218 | Cite as

Replicating data objects in large distributed database systems: an axiomatic game theoretic mechanism design approach

  • Samee Ullah Khan
  • Ishfaq Ahmad
Article

Abstract

Data object replication onto distributed servers can potentially alleviate bottlenecks, reduce network traffic, increase scalability, add robustness, and decrease user perceived access time. The decision of selecting data object and server pairs requires solving a constraint optimization problem that in general is NP-complete. In this paper, we abstract the distributed database system as an agent-based model, wherein agents continuously compete for allocation and reallocation of data objects. Each agent aims to replicate objects onto its server such that the communication cost is minimized. However, these agents do not have a global view of the system. Thereby, the optimization process becomes highly localized. Such localized optimization may severely affect the overall system performance. To cope with such localized optimization, we propose a “semi-distributed” axiomatic game theoretical mechanism. The mechanism’s control is unique in its decision making process, wherein all the heavy processing is done on the servers of the distributed system and the central body is only required to take a binary decision: (0) not to replicate or (1) to replicate. The cost model used by the agents in the mechanism for the purpose of identifying beneficial data objects is tailored made so that even though the agents take decisions based on their local knowledge domain, the effect is translated into a system-wide performance enhancement. The proposed mechanism is extensively compared against seven well-known conventional and three game theoretical replica allocation methods, namely, branch and bound, greedy, genetic, data-aware replication, tree inspired bottom-up procedure, tree inspired min-max procedure, Benders’ decomposition based procedure, game theoretical English auction, game theoretical Dutch auction, and game theoretical selfish replication procedure. The experimental setup incorporates GT-ITM, Inet network topology generators, Soccer World Cup 1998 access logs, and NASA Kennedy Space Center access logs to closely mimic the Web in its infrastructure and user access patterns. The experimental results reveal that the proposed technique despite its non-cooperative nature improves the solution quality and reduces the execution time compared to other techniques.

Keywords

Algorithmic mechanism design Game theory Optimization Replication Distributed algorithms Placements 

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References

  1. 1.
    Ahmad, I., Ghafoor, A.: Semi-distributed load balancing for massively parallel multicomputer systems. IEEE Trans. Softw. Eng. 17(10), 987–1004 (1991) CrossRefMathSciNetGoogle Scholar
  2. 2.
    Arlitt, M., Jin, T.: Workload characterization of the 1998 World Cup Web Site. Tech. Report, Hewlett Packard Lab, Palo Alto, HPL-1999-35(R.1) (1999) Google Scholar
  3. 3.
    Awerbuch, B., Bartal, Y., Fiat, A.: Competitive distributed file allocation. In: Proc. 25th ACM Symposium on Theory of Computation, pp. 164–173 (1993) Google Scholar
  4. 4.
    Bekta, T., Cordeaua, J.-F., Erkutc, E., Laportea, G.: Exact algorithms for the joint object placement and request routing problem in content distribution networks. Comput. Oper. Res. 35, 3860–3884 (2008) CrossRefGoogle Scholar
  5. 5.
    Briest, P., Krysta, P., Vöcking, B.: Approximation techniques for utilitarian mechanism design. In: Proc. of 37th ACM Symposium on Theory of Computation, pp. 39–48 (2005) Google Scholar
  6. 6.
    Campbell, D.: Resource Allocation Mechanisms. Cambridge University Press, Cambridge (1987) Google Scholar
  7. 7.
    Casey, R.: Allocation of copies of a file in an information network. In: Proc. Spring Joint Computer Conf., IFIPS, pp. 617–625 (1972) Google Scholar
  8. 8.
    Chandy, J.A.: A generalized replica placement strategy to optimize latency in a wide area distributed storage system. In: International Workshop on Data-Aware Distributed Computing, pp. 49–54 (2008) Google Scholar
  9. 9.
    Chandy, K., Hewes, J.: File allocation in distributed systems. In: Proc. of the International Symposium on Computer Performance Modeling, Measurement and Evaluation, pp. 10–13 (1976) Google Scholar
  10. 10.
    Chang, H., Govindan, R., Jamin, S., Shenker, S.: Towards capturing representative AS-level Internet topologies. Comput. Netw. J. 44(6), 737–755 (2004) CrossRefGoogle Scholar
  11. 11.
    Chu, W.: Optimal file allocation in a multiple computer system. IEEE Trans. Comput. C-18(10), 885–889 (1969) CrossRefGoogle Scholar
  12. 12.
    Chun, B.-G., Chaudhuri, K., Wee, H., Barreno, M., Papadimitriou, C., Kubiatowicz, J.: Selfish caching in distributed systems: a game-theoretic analysis. In: Proc. of 23rd ACM Symposium on Principles of Distributed Computing, pp. 21–30 (2004) Google Scholar
  13. 13.
    Cidon, I., Kutten, S., Soffer, R.: Optimal allocation of electronic content. In: Proc. of IEEE INFOCOM, pp. 1773–1780 (2001) Google Scholar
  14. 14.
    Cook, S., Pachl, J., Pressman, I.: The optimal location of replicas in a network using a READ-ONE-WRITE-ALL policy. Distrib. Comput. 15(1), 57–66 (2002) CrossRefGoogle Scholar
  15. 15.
    Dowdy, L., Foster, D.: Comparative models of the file assignment problem. ACM Comput. Surv. 14(2), 287–313 (1982) CrossRefGoogle Scholar
  16. 16.
    Eswaran, K.P: Placement of records in a file and file allocation in a computer. In: Proc. of IFIP Congress, pp. 304–307 (1974) Google Scholar
  17. 17.
    Green, J., Laffont, J.: Characterization of satisfactory mechanisms for the revelation of preferences for public goods. Econometrica 45(2), 427–438 (1977) MATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Groves, T.: Incentives in teams. Econometrica 41, 617–631 (1973) MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Habegger, P., Bieri, H.: Modeling the topology of the Internet: an assessment. Tech. Report, Institut für Informatik und angewandte Mathematik, Universität Bern, IM-02-2002 Google Scholar
  20. 20.
    Hakimi, S.: Optimum location of switching centers and the absolute centers and medians of a graph. Oper. Res. 12, 450–459 (1964) MATHCrossRefGoogle Scholar
  21. 21.
    Hara, T.: Effective replica allocation in ad hoc networks for improving data accessibility. In: Proc. of INFOCOM, pp. 1568–1576 (2001) Google Scholar
  22. 22.
    Heddaya, A., Mirdad, S.: WebWave: globally load balanced fully distributed caching of hot published documents. In: Proc. 17th International Conference on Distributed Computing Systems, pp. 160–168 (1997) Google Scholar
  23. 23.
    Jamin, S., Jin, C., Jin, Y., Riaz, D., Shavitt, Y., Zhang, L.: On the placement of Internet instrumentation. In: Proc. of the IEEE INFOCOM, pp. 295–304 (2000) Google Scholar
  24. 24.
    Jamin, S., Jin, C., Kurc, T., Raz, D., Shavitt, Y.: Constrained mirror placement on the Internet. In: Proc. of the IEEE INFOCOM, pp. 31–40 (2001) Google Scholar
  25. 25.
    Kalpakis, K., Dasgupta, K., Wolfson, O.: Optimal placement of replicas in trees with read, write, and storage costs. IEEE Trans. Parallel Distrib. Syst. 12(6), 628–637 (2001) CrossRefGoogle Scholar
  26. 26.
    Kangasharju, J., Roberts, J., Ross, K.: Object replication strategies in content distribution networks. In: Proc. of Web Caching and Content Distribution Workshop, pp. 455–456 (2001) Google Scholar
  27. 27.
    Karlsson, M., Mahalingam, M.: Do we need replica placement algorithms in content delivery networks? In: Proc. of Web Caching and Content Distribution Workshop, pp. 117–128 (2002) Google Scholar
  28. 28.
    Khan, S., Ahmad, I.: Internet content replication: a solution from game theory. Technical Report, University of Texas at Arlington, CSE-2004-5 (2004) Google Scholar
  29. 29.
    Khan, S., Ahmad, I.: Heuristic-based replication schemas for fast information retrieval over the Internet. In: Proc. of 17th International Conference on Parallel and Distributed Computing Systems, pp. 278–283 (2004) Google Scholar
  30. 30.
    Khan, S., Ahmad, I.: A powerful direct mechanism for optimal WWW content replication. In: Proc. of 19th IEEE International Parallel and Distributed Processing Symposium (2005) Google Scholar
  31. 31.
    Khan, S.U., Ahmad, I.: RAMM: a game theoretical replica allocation and management mechanism. In: 8th International Symposium on Parallel Architectures, Algorithms, and Networks (I-SPAN), Las Vegas, NV, USA, pp. 160–165, December 2005 Google Scholar
  32. 32.
    Khan, S.U., Ahmad, I.: A pure nash equilibrium guaranteeing game theoretical replica allocation method for reducing web access time. In: 12th International Conference on Parallel and Distributed Systems (ICPADS), Minneapolis, MN, USA, pp. 169–176, July 2006 Google Scholar
  33. 33.
    Khan, S.U., Ahmad, I.: Replicating data objects in large-scale distributed computing systems using extended Vickery auction. Int. J. Comput. Intell. 3(1), 14–22 (2006) Google Scholar
  34. 34.
    Khan, S.U., Ahmad, I.: Discriminatory algorithmic mechanism design based WWW content replication. Informatica 31(1), 105–119 (2007) MathSciNetGoogle Scholar
  35. 35.
    Khan, S.U., Ahmad, I.: A cooperative game theoretical replica placement technique. In: 13th International Conference on Parallel and Distributed Systems (ICPADS), Hsinchu, Taiwan, December 2007 Google Scholar
  36. 36.
    Korupolu, M., Plaxton, C.: Analysis of a local search heuristic for facility location problems. J. Algorithms 37(1), 146–188 (2000) MATHCrossRefMathSciNetGoogle Scholar
  37. 37.
    Krick, C., Racke, H., Westermann, M.: Approximation algorithms for data management in networks. In: Proc. of the Symposium on Parallel Algorithms and Architecture, pp. 237–246 (2001) Google Scholar
  38. 38.
    Krishna, V.: Auction Theory. Academic Press, San Diego (2002) Google Scholar
  39. 39.
    Krishnan, P., Raz, D., Shavitt, Y.: The cache location problem. IEEE/ACM Trans. Netw. 8(5), 568–582 (2000) CrossRefGoogle Scholar
  40. 40.
    Kurose, J., Simha, R.: A microeconomic approach to optimal resource allocation in distributed computer systems. IEEE Trans. Comput. 38(5), 705–717 (1989) CrossRefGoogle Scholar
  41. 41.
    Laoutaris, N., Telelis, O., Zissimopoulos, V.: Distributed selfish replication. IEEE Trans. Parallel Distrib. Syst. 17(12), 1401–1413 (2006) CrossRefGoogle Scholar
  42. 42.
    Li, B., Golin, M., Italiano, G., Deng, X.: On the optimal placement of web proxies in the Internet. In: Proc. of the IEEE INFOCOM, pp. 1282–1290 (2000) Google Scholar
  43. 43.
    Lin, Y.-F., Liu, P., Wu, J.-J.: Optimal placement of replicas in data grid environments with locality assurance. In: 12th International Conference on Parallel and Distributed Systems (ICPADS), pp. 465–474 (2006) Google Scholar
  44. 44.
    Loukopoulos, T., Papadias, D., Ahmad, I.: An overview of data replication on the Internet. In: Proc. of International Symposium on Parallel Architectures, Algorithms and Networks, pp. 31–38 (2002) Google Scholar
  45. 45.
    Loukopoulos, T., Ahmad, I.: Static and adaptive distributed data replication using genetic algorithms. J. Parallel Distrib. Comput. 64(11), 1270–1285 (2004) MATHCrossRefGoogle Scholar
  46. 46.
    Mahmoud, S., Riordon, J.: Optimal allocation of resources in distributed information networks. ACM Trans. Database Syst. 1(1), 66–78 (1976) CrossRefGoogle Scholar
  47. 47.
    Mas-Collel, A., Whinston, W., Green, J.: Microeconomic Theory. Oxford University Press, London (1995) Google Scholar
  48. 48.
    Medina, A., Matta, I., Byers, J.: On the origin of power laws in Internet topologies. ACM Comput. Commun. Rev. 30(2), 18–28 (2000) CrossRefGoogle Scholar
  49. 49.
    Narebdran, B., Rangarajan, S., Yajnik, S.: Data distribution algorithms for load balancing fault-tolerant web access. In: Proc. of the 16th Symposium on Reliable Distributed Systems, pp. 97–106 (1997) Google Scholar
  50. 50.
    NASA Kennedy Space Center access log. Available at: http://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html
  51. 51.
    Nisan, N., Ronen, A.: Algorithmic mechanism design. In: Proc. of 31st ACM Symposium on Theory of Computation, pp. 129–140 (1999) Google Scholar
  52. 52.
    Osborne, M., Rubinstein, A.: A Course in Game Theory. MIT Press, Cambridge (1994) MATHGoogle Scholar
  53. 53.
    Pautet, L., Tardieu, S.: GLADE: a framework for building large object-oriented real-time distributed systems. In: 3rd International Symposium on Object-Oriented Real-Time Distributed Systems, pp. 244–251 (2000) Google Scholar
  54. 54.
    Qiu, L., Padmanabhan, V., Voelker, G.: On the placement of web server replicas. In: Proc. of the IEEE INFOCOM, pp. 1587–1596 (2001) Google Scholar
  55. 55.
    Rabinovich, M.: Issues in Web content replication. Data Eng. Bull. 21(4), 21–29 (1998) Google Scholar
  56. 56.
    Radoslavov, P., Govindan, R., Estrin, D.: Topology-informed Internet replica placement. Comput. Commun. 25(4), 384–392 (2002) CrossRefGoogle Scholar
  57. 57.
    Saurabh, S., Parkes, D.: Hard-to-manipulate VCG-based auctions. Available at: http://www.eecs.harvard.edu/econcs/pubs/hard_to_manipulate.pdf
  58. 58.
    Venkataramanj, A., Weidmann, P., Dahlin, M.: Bandwidth constrained placement in a WAN. In: Proc. ACM Symposium on Principles of Distributed Computing, pp. 134–143 (2001) Google Scholar
  59. 59.
    Waxman, B.: Routing of multipoint connections. IEEE J. Sel. Areas Commun. 6(9), 1617–1622 (1988) CrossRefGoogle Scholar
  60. 60.
    Wolfson, O., Jajodia, S., Hang, Y.: An adaptive data replication algorithm. ACM Trans. Database Syst. 22(4), 255–314 (1997) CrossRefGoogle Scholar
  61. 61.
    Zegura, E., Calvert, K., Donahoo, M.: A quantitative comparison of graph-based models for Internet topology. IEEE/ACM Trans. Netw. 5(6), 770–783 (1997) CrossRefGoogle Scholar
  62. 62.
    Zipf, G.: Human Behavior and the Principle of Least-Effort. Addison-Wesley, Reading (1949) Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringNorth Dakota State UniversityFargoUSA
  2. 2.Department of Computer Science and EngineeringUniversity of TexasArlingtonUSA

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