http://www.cnr.it/ontology/cnr/individuo/prodotto/ID44163
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
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Anticaglia S.; Barsi F.; Bertossi A.; Iamele L.; Pinotti M. C. (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- 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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