Int-FLBCC: Model for Load Balancing in Cloud Computing using Fuzzy Logic Type-2 and Admissible Orders.

Authors

  • Guilherme Bayer Schneider Centro de Desenvolvimento Tecnológico (CDTEC), Universidade Federal de Pelotas (UFPel), Pelotas, Rio Grande do Sul, Brasil
  • Bruno Moura Paz de Moura Centro de Desenvolvimento Tecnológico (CDTEC), Universidade Federal de Pelotas (UFPel), Pelotas, Rio Grande do Sul, Brasil http://orcid.org/0000-0002-3960-8377
  • Adenauer C Yamin Centro de Desenvolvimento Tecnológico (CDTEC), Universidade Federal de Pelotas (UFPel), Pelotas, Rio Grande do Sul, Brasil http://orcid.org/0000-0002-7333-244X
  • Renata Hax Sander Reiser Centro de Desenvolvimento Tecnológico (CDTEC), Universidade Federal de Pelotas (UFPel), Pelotas, Rio Grande do Sul, Brasil http://orcid.org/0000-0001-9934-3115

DOI:

https://doi.org/10.22456/2175-2745.98362

Keywords:

Fuzzy logic, Uncertainty, Cloud computing, Resource management, Data centers, Random access memory, Quality of service

Abstract

Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing, directly affecting Quality of Service aspects. This paper presents Int-FLBCC, a new proposal with exploring a Type-2 Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. Validation results in a simulated cloud computing environment showed improvements in energy efficiency of 8.83% with IQR_XY and 22.43% with MAD_XY. For fulfillment of Service Level Agreements (SLA), the best values achieved were 9.06% with MAD_XY and 25% of THR_Lex1.

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Published

2020-06-18

How to Cite

Schneider, G. B., Moura Paz de Moura, B., Yamin, A. C., & Hax Sander Reiser, R. (2020). Int-FLBCC: Model for Load Balancing in Cloud Computing using Fuzzy Logic Type-2 and Admissible Orders. Revista De Informática Teórica E Aplicada, 27(3), 102–117. https://doi.org/10.22456/2175-2745.98362

Issue

Section

Selected Papers - WEIT 2019