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An adaptive context-aware optimization framework for multimedia adaptation service selection

  • S.I. : Emerging trends in AI & ML
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Abstract

In pervasive systems, context is a direct cause to adapt the content of multimedia documents so that they comply, as far as possible, with the current constraints. In this respect, several adaptation approaches have already been proposed, in which adaptation services are often selected from shortlists of services. Practically speaking, adaptation services are provided in various instances and ways, thus making the selection task more difficult. Furthermore, existing approaches for the service selection paradigm cannot be properly applied mainly because constraints on execution time and the availability of computation resources must be considered. To deal with this issue, we propose a framework for adaptive service selection using a bag of metaheuristics ranging from local to global search methods. Depending on the contextual constraints, a sub-bag of algorithms is selected, for which the budget is distributed, using a reinforcement learning mechanism related to their performances. The proposal is validated through a set of experiments and comparisons. The obtained results are satisfactory and encouraging.

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Acknowledgements

This work was partially supported by the Direction Générale de la Recherche Scientifique et du Développement Technologique (DGRSDT) in Algeria.

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Authors and Affiliations

  1. Research Laboratory on Computer Science’s Complex Systems (ReLa(CS)2), University of Oum El Bouaghi, Oum El Bouaghi, Algeria

    Zakaria Laboudi, Asma Saighi & Selma Hadjadj

  2. Department of Fundamental Computer Science and its Applications, University of Constantine 2, Constantine, Algeria

    Abdelkader Moudjari & Amer Draa

Authors
  1. Zakaria Laboudi
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  2. Abdelkader Moudjari
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  3. Asma Saighi
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  4. Amer Draa
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  5. Selma Hadjadj
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Correspondence to Zakaria Laboudi.

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Laboudi, Z., Moudjari, A., Saighi, A. et al. An adaptive context-aware optimization framework for multimedia adaptation service selection. Neural Comput & Applic 34, 14239–14251 (2022). https://doi.org/10.1007/s00521-021-06644-w

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