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Int-FLBCC: Exploring Fuzzy Consensus Measures via Penalty Functions

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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1601))

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Abstract

The dynamic consolidation of resources in the infrastructures of services, programs, and information provided by cloud environments is a widely used strategy, modeling uncertainties to improve energy consumption in cloud computing. Determining the best configuration to reallocate overloaded hosts, underutilized or/and shallow load nodes may directly influence the resource utilization and the quality of service offered by the cloud-computing infrastructure. In this scenario, this work aims to address the uncertainty information related to computational power, communication cost, and RAM consumption in cloud environments based on the Int-FLBCC model. An interval-valued fuzzy logic approach is used, assuring reliability in the evaluation data through fuzzy consensus measures. The consensual analysis considers fusion data based on penalty functions. The evaluations considered two approaches: (i) consensus measures and penalty functions in fuzzy values related to membership functions; and (ii) consensus measures performed on fuzzy sets defining the input and output variables, building a new consensual analysis modeling the cohesion of several terms related to the same linguistic variables, and the coherence between fuzzy sets referring to the lowest and highest projections. Simulations pointed to promising results in the treatment of imprecision in Int-FLBCC.

This study was partially supported by CAPES, CNPq (309160/2019-7; 311429/2020-3), PqG/FAPERGS (21/2551-0002057-1) and FAPERGS/CNPq PRONEX (16/2551-0000488-9).

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Notes

  1. 1.

    https://www.nrdc.org/.

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Author information

Authors and Affiliations

  1. Universidade Federal de Pelotas, Pelotas, Brazil

    Guilherme Schneider, Bruno Moura, Eduardo Monks, Adenauer Yamin & Renata Reiser

  2. Universidade Federal do Rio Grande, Rio Grande, Brazil

    Helida Santos

  3. Universidad Publica de Navarra - ISC, Pamplona, Spain

    Helida Santos

Authors
  1. Guilherme Schneider
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  2. Bruno Moura
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  3. Eduardo Monks
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  4. Helida Santos
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  5. Adenauer Yamin
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  6. Renata Reiser
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Corresponding author

Correspondence to Helida Santos .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

  2. University of Oviedo, Oviedo, Spain

    Inés Couso

  3. University of Cádiz, Cádiz, Spain

    Jesús Medina

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. University of Perugia, Perugia, Italy

    Davide Petturiti

  6. Sorbonne Université, Paris, France

    Bernadette Bouchon-Meunier

  7. Iona College, New Rochelle, NY, USA

    Ronald R. Yager

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Schneider, G., Moura, B., Monks, E., Santos, H., Yamin, A., Reiser, R. (2022). Int-FLBCC: Exploring Fuzzy Consensus Measures via Penalty Functions. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1601. Springer, Cham. https://doi.org/10.1007/978-3-031-08971-8_36

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  • DOI: https://doi.org/10.1007/978-3-031-08971-8_36

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