Improved Approximation Algorithms for Resource Allocation

  • Gruia Calinescu
  • Amit Chakrabarti
  • Howard Karloff
  • Yuval Rabani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2337)


We study the problem of finding a most profitable subset of n given tasks, each with a given start and finish time as well as profit and resource requirement, that at no time exceeds the quantity B of available resource. We show that this NP-hard Resource Allocation problem can be (1/2 - ε)-approximated in polynomial time, which improves upon earlier approximation results for this problem, the best previously published result being a 1/4-approximation. We also give a simpler and faster 1/3-approximation algorithm.


Interval Graph Optimal Packing Call Admission Control Large Task Improve Approximation Algorithm 
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  1. 1.
    E. M. Arkin, E. B. Silverberg. Scheduling jobs with fixed start and end times. Discrete Applied Mathematics, 18 (1987), 1–8.MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    A. Bar-Noy, R. Bar-Yehuda, A. Freund, J. Naor, B. Schieber. A unified approach to approximating resource allocation and scheduling. In Proceedings of the 32nd Annual ACM Symposium on Theory of Computing (2000), 735–744.Google Scholar
  3. 3.
    A. Bar-Noy. Private communication (2001).Google Scholar
  4. 4.
    B. Chen, R. Hassin, M. Tzur. Allocation of bandwidth and storage. IIE Transactions, 34 (2002), 501–507.Google Scholar
  5. 5.
    N. G. Hall, M. J. Magazine. Maximizing the value of a space mission. European Journal of Operational Research, 78 (1994), 224–241.zbMATHCrossRefGoogle Scholar
  6. 6.
    S. Leonardi, A. Marchetti-Spaccamela, A. Vitaletti. Approximation algorithms for bandwidth and storage allocation problems under real time constraints. In Proceedings of the 20th Conference on Foundations of Software Technology and Theoretical Computer Science (2000), 409–420.Google Scholar
  7. 7.
    J. B. Orlin. A faster strongly polynomial minimum cost flow algorithm. Operations Research, 41 (1993), 338–350.MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    C. Phillips, R N. Uma, J. Wein. Off-line admission control for general scheduling problems. In Proceedings of the 11th Annual ACM-SIAM Symposium on Discrete Algorithms (2000), 879–888.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Gruia Calinescu
    • 1
  • Amit Chakrabarti
    • 2
  • Howard Karloff
    • 3
  • Yuval Rabani
    • 4
  1. 1.Department of Computer ScienceIllinois Institute of TechnologyChicago
  2. 2.Department of Computer SciencePrinceton UniversityPrinceton
  3. 3.AT&T Labs — ResearchFlorham Park
  4. 4.Computer Science DepartmentTechnion — IITHaifaIsrael

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