Efficient heuristics for data broadcasting on multiple channels (Articolo in rivista)

Type
Label
  • Efficient heuristics for data broadcasting on multiple channels (Articolo in rivista) (literal)
Anno
  • 2008-01-01T00:00:00+01:00 (literal)
Alternative label
  • Anticaglia S.; Barsi F.; Bertossi A.; Iamele L.; Pinotti M. C. (2008)
    Efficient heuristics for data broadcasting on multiple channels
    in Wireless networks
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Anticaglia S.; Barsi F.; Bertossi A.; Iamele L.; Pinotti M. C. (literal)
Pagina inizio
  • 219 (literal)
Pagina fine
  • 231 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 14 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: Wireless Networks, vol. 14 (2) pp. 219 - 231. Springer, 2008. (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Department of Computer Science and Mathematics, University of Perugia, CNR-ISTI, Pisa (literal)
Titolo
  • Efficient heuristics for data broadcasting on multiple channels (literal)
Abstract
  • The problem of data broadcasting over multi- ple channels consists in partitioning data among channels, depending on data popularities, and then cyclically trans- mitting them over each channel so that the average wait- ing time of the clients is minimized. Such a problem is known to be polynomially time solvable for uniform length data items, while it is computationally intractable for non- uniform length data items. In this paper, two new heuris- tics are proposed which exploit a novel characterization of optimal solutions for the special case of two channels and data items of uniform lengths. Sub-optimal solutions for the most general case of an arbitrary number of channels and data items of non-uniform lengths are provided. The first heuristic, called Greedy +, combines the novel characteri- zation with the known greedy approach, while the second heuristic, called Dlinear, combines the same characteriza- tion with the dynamic programming technique. Such heuris- tics have been tested on benchmarks whose popularities are characterized by Zipf distributions, as well as on a wider set of benchmarks. The experimental tests reveal that Dlinear finds optimal solutions almost always, requiring good run- ning times. However, Greedy + is faster and scales well when changes occur on the input parameters, but provides solutions which are close to the optimum. (literal)
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