Replicating Multi-quality Web Applications Using ACO and Bipartite Graphs

  • Christopher B. Mayer
  • Judson Dressler
  • Felicia Harlow
  • Gregory Brault
  • K. Selçuk Candan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)


This paper presents the application of the Ant Colony Optimization (ACO) meta-heuristic to a new NP-hard problem involving the replication of multi-quality database-driven web applications (DA s) by a large application service provider (ASP). The ASP must assign DA replicas to its network of heterogeneous servers so that user demand is satisfied at the desired quality level and replica update loads are minimized. Our ACO algorithm, AntDA , for solving the ASP’s replication problem has several novel or infrequently seen features: ants traverse a bipartite graph in both directions as they construct solutions, pheromone is used for traversing from one side of the bipartite graph to the other and back again, heuristic edge values change as ants construct solutions, and ants may sometimes produce infeasible solutions. Testing shows that the best results are achieved by using pheromone and heuristics to traverse the bipartite graph in both directions. Additionally, experiments show that AntDA outperforms several other solution methods.


Bipartite Graph Quality Level Knapsack Problem Infeasible Solution Optimization Heuristic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bright, L., Raschid, L.: Using latency-recency profiles for data delivery on the web. In: VLDB, pp. 550–561 (2002)Google Scholar
  2. 2.
    Cherniack, M., Galvez, E.F., Franklin, M.J., Zdonik, S.: Profile-driven cache management. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 645–656. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  3. 3.
    Mayer, C.B.: Quality-based Replication of Freshness-Differentiated Web Applications and Their Back-end Databases. Ph.D thesis, Arizona State University (2005)Google Scholar
  4. 4.
    Mazzola, J.B., Neebe, A.W.: Lagrangian-relaxation-based solution procedures for a multiproduct capacitated facility location problem with choice of facility type. European Journal of Operational Research 115, 285–299 (1999)MATHCrossRefGoogle Scholar
  5. 5.
    Pirkul, H., Jayaraman, V.: A multi-commodity, multi-plant, capacitated facility location problem: Formulation and efficient heuristic solution. Computers and Operations Research 25(10), 869–878 (1998)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Dawande, M., Kalagnanam, J., Keskinocak, P., Ravi, R., Salman, F.S.: Approximation algorithms for the multiple knapsack problem with assignment restrictions. Combinatorial Optimization 4(2), 171–186 (2000)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Shachnai, H., Tamir, T.: Noah’s bagels - some combinatorial aspects. In: International Conference on Fun with Algorithms (1998)Google Scholar
  8. 8.
    Alaya, I., Solnon, C., Ghédira, K.: Ant algorithm for the multidimensional knapsack problem. In: International Conference on Bioinspired Optimization Methods and their Applications, pp. 63–72 (2004)Google Scholar
  9. 9.
    Leguizamon, G., Michalewicz, Z.: A new version of ant system for subset problems. In: Proceeding of the 1999 Congress on Evolutionary Computation, pp. 1459–1464. IEEE Press, Los Alamitos (1999)Google Scholar
  10. 10.
    Cordón, O., Fernández de Viana, I., Herrera, F.: Analysis of the best-worst ant system and its variants on the QAP. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 228–234. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Foong, W.K., Maier, H.R., Simpson, A.R.: Ant colony optimization for power plant maintenance scheduling optimization. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 249–256. ACM, New York (2005)CrossRefGoogle Scholar
  12. 12.
    Lourenço, H.R., Serra, D.: Adaptive search heuristics for the generalized assignment problem. Mathware and Soft Computing 9(2), 209–234 (2002), On-line journal. Articles available at: MATHMathSciNetGoogle Scholar
  13. 13.
    Stützle, T., Dorigo, M.: ACO algorithms for the quadratic assignment problem. In: New Ideas in Optimization, pp. 33–50. McGraw-Hill, New York (1999)Google Scholar
  14. 14.
    Bonabeau, E., Dorigo, M., Theraulaz, T.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)MATHGoogle Scholar
  15. 15.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. The MIT Press, Cambridge (2004)MATHCrossRefGoogle Scholar
  16. 16.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics–Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  17. 17.
    Eyckelhof, C.J., Snoek, M.: Ant systems for a dynamic TSP. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 88–99. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  18. 18.
    Blum, C., Sampels, M.: An ant colony optimization algorithm for shop scheduling problems. J. of Mathematical Modelling and Algorithms 34(3), 285–308 (2004)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Comellas, F., Ozón, J.: An ant algorithm for the graph colouring problem. In: First International Workshop on Ant Colony Optimization (ANTS 1998) (1998)Google Scholar
  20. 20.
    Costa, D., Hertz, A.: Ants can colour graphs. J. of the Operational Research Society 48, 295–305 (1997)MATHGoogle Scholar
  21. 21.
    Gambardella, L.M., Dorigo, M.: An ant colony system hybridized with a new local search for the sequential ordering problem. INFORMS Journal on Computing 12(3), 237–255 (2000)MATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    LINDO Systems, Inc.: Lingo (2006),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christopher B. Mayer
    • 1
  • Judson Dressler
    • 1
  • Felicia Harlow
    • 1
  • Gregory Brault
    • 1
  • K. Selçuk Candan
    • 2
  1. 1.Department of Electrical and Computer EngineeringAir Force Institute of TechnologyWright-Patterson AFBUSA
  2. 2.Computer Science and Engineering DepartmentArizona State UniversityTempeUSA

Personalised recommendations