Mixed Fault Tolerance in Server Assignment: Combining Reinforcement and Backup

  • Tal Navon
  • David PelegEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11085)


We study the mixed approach to fault tolerance in the general context of server assignment in networks. The approach is based on mixing two different existing strategies, namely, reinforcement and backup. The former strategy protects clients by reinforcing the servers assigned to them and making them fault-resistant (at a possibly high cost), while the latter protects clients by assigning to them alternate low price backup servers that can replace their primary servers in case those fail. Applying the mixed approach to fault tolerance gives rise to new fault-tolerant variations of known server assignment problems. We introduce several NP-hard problems of this type, including the mixed fault-tolerant dominating set problem, the mixed fault-tolerant centers problem, and the mixed fault-tolerant facility location problem, and present polynomial time approximation algorithms for them, demonstrating the viability of the mixed strategy for server assignment problems.


Approximation Fault tolerance Backup Reinforcement Server assignment problems Dominating set Centers Facility location 


  1. 1.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Co., New York (1979)Google Scholar
  2. 2.
    Gonzalez, T.F.: Clustering to minimize the maximum intercluster distance. Theor. Comput. Sci. 38, 293–306 (1985)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Hochbaum, D.S., Shmoys, D.B.: A best possible heuristic for the k-center problem. Math. Oper. Res. 10(2), 180–184 (1985)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Hochbaum, D.S., Shmoys, D.B.: A unified approach to approximation algorithms for bottleneck problems. J. ACM 33(3), 533–550 (1986)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Johnson, D.S.: Approximation algorithms for combinatorial problems. J. Comput. Syst. Sci. 9(3), 256–278 (1974)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Khuller, S., Pless, R., Sussmann, Y.J.: Fault tolerant k-center problems. Theor. Comput. Sci. 242(1), 237–245 (2000)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Parter, M., Peleg, D.: Fault tolerant BFS structures: a reinforcement-backup tradeoff. In: 27th ACM Symposium on Parallel Algorithms and Architectures (2015)Google Scholar
  8. 8.
    Schrijver, A.: Theory of Linear and Integer Programming. Wiley, Wiley-Interscience Series in Discrete Mathematics and Optimization (1999)Google Scholar
  9. 9.
    Sviridenko, M.: An improved approximation algorithm for the metric uncapacitated facility location problem. In: Cook, W.J., Schulz, A.S. (eds.) IPCO 2002. LNCS, vol. 2337, pp. 240–257. Springer, Heidelberg (2002). Scholar
  10. 10.
    Swamy, C., Shmoys, D.B.: Fault-tolerant facility location. ACM Trans. Algorithms (TALG) 4(4), 51 (2008)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Wolsey, L.A.: An analysis of the greedy algorithm for the submodular set covering problem. Combinatorica 2(4), 385–393 (1982)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.The Weizmann InstituteRehovotIsrael

Personalised recommendations