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On Packing Splittable Items with Cardinality Constraints

  • Fouad B. Chedid
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 323)

Abstract

This paper continues the study of the the allocation of memory to processors in a pipeline problem. This problem can be modeled as a variation of bin packing where each item corresponds to a different type and the normalized weight of each item can be greater than 1, which is the size of a bin. Furthermore, in this problem, items may be split arbitrarily, but each bin may contain at most k types of items, for any fixed integer k ≥ 2. The case of k = 2 was first introduced by Chung el al. who gave a 3/2-approximation asymptotically. In this paper, we generalize the result of Chung et al. to higher k. We show that NEXT FIT gives a \(\left(1+\frac 1 k\right)\)-approximation asymptotically, for k ≥ 2. Also, as a minor contribution, we rewrite the strong NP-hardness proof of Epstein and van Stee for this problem for k ≥ 3.

Keywords

Approximation Ratio Cardinality Constraint Memory Request Lookup Time Splittable Item 
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.

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Copyright information

© IFIP 2010

Authors and Affiliations

  • Fouad B. Chedid
    • 1
  1. 1.Department of Computer ScienceNotre Dame UniversityZouk MikaelLebanon

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