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


  • 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
  • Adenauer C Yamin Centro de Desenvolvimento Tecnológico (CDTEC), Universidade Federal de Pelotas (UFPel), Pelotas, Rio Grande do Sul, Brasil
  • Renata Hax Sander Reiser Centro de Desenvolvimento Tecnológico (CDTEC), Universidade Federal de Pelotas (UFPel), Pelotas, Rio Grande do Sul, Brasil



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


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.


Download data is not yet available.


TOOSI, A. N.; BUYYA, R. A fuzzy logic-based controller for cost and energy efficient load balancing in geodistributed data centers. In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC). [S.l.: s.n.], 2015. p. 186–194.

MELL, P.; GRANCE, T. et al. The nist definition of cloud computing. Computer Security Division, Information Technology Laboratory, National . . . , 2011.

SHEHABI, A. et al. United states data center energy usage report. 2016.

NATHANI, A.; CHAUDHARY, S.; SOMANI, G. Policy based resource allocation in iaas cloud. Future Generation Computer Systems, Elsevier, v. 28, n. 1, p. 94–103, 2012.

BELOGLAZOV, A.; BUYYA, R. Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Transactions on Parallel and Distributed Systems, v. 24, n. 7, p. 1366–1379, July 2013.

ZHANG, Q.; CHENG, L.; BOUTABA, R. Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, v. 1, n. 1, p. 7–18, May 2010. Disponível em:

HARATIAN, P. et al. An adaptive and fuzzy resource management approach in cloud computing. IEEE Transactions on Cloud Computing, p. 1–1, 2018.

MOURA, B. M. P. et al. Int-fGrid: BoT Tasks Scheduling Exploring Fuzzy Type-2 in Computational Grids. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). [S.l.: s.n.], 2018. p. 1–8.

HAMDAQA, M.; TAHVILDARI, L. Cloud computing uncovered: a research landscape. In: Advances in Computers. [S.l.]: Elsevier, 2012. v. 86, p. 41–85.

HOLICK, M. F. et al. Evaluation, treatment, and prevention of vitamin d deficiency: an endocrine society clinical practice guideline. The Journal of Clinical Endocrinology & Metabolism, Oxford University Press, v. 96, n. 7, p. 1911–1930, 2011.

CHIEU, T. C. et al. Dynamic scaling of web applications in a virtualized cloud computing environment. In: IEEE. 2009 IEEE International Conference on e-Business Engineering. [S.l.], 2009. p. 281–286.

BATISTA, B. G. Modelos de negócio para ambientes de computação em nuvem que consideram atributos de qos relacionados a desempenho e a segurança. Tese (Doutorado) — Universidade de São Paulo, 2016.

ZHANG, Q.; CHENG, L.; BOUTABA, R. Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, Springer, v. 1, n. 1, p. 7–18, 2010.

FERDAUS, M. H. et al. Virtual machine consolidation in cloud data centers using aco metaheuristic. In: SPRINGER. European conference on parallel processing. [S.l.], 2014. p. 306–317.

AHMED, A.; SABYASACHI, A. S. Cloud computing simulators: A detailed survey and future direction. In: IEEE. Advance Computing Conference (IACC), 2014 IEEE International. [S.l.], 2014. p. 866–872.

KLIAZOVICH, D.; BOUVRY, P.; KHAN, S. U. Green-cloud: a packet-level simulator of energy-aware cloud computing data centers. The Journal of Supercomputing, Springer, v. 62, n. 3, p. 1263–1283, 2012.

WICKREMASINGHE, B.; CALHEIROS, R. N.; BUYYA, R. Cloudanalyst: A cloudsim-based visual modeller for analysing cloud computing environments and applications. In: IEEE. Advanced Information Networking and Applications (AINA), 2010 24th IEEE International Conference on. [S.l.], 2010. p. 446–452.

CALHEIROS, R. N. et al. Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience, Wiley Online Library, v. 41, n. 1, p. 23–50, 2011.

ZADEH, L. The concept of a linguistic variable and its application to approximate reasoning—i. Information Sciences, v. 8, n. 3, p. 199 – 249, 1975.

MENDEL, J. M. Fuzzy sets for words: a new beginning. In: Fuzzy Systems, 2003. FUZZ ’03. The 12th IEEE International Conference on. [S.l.: s.n.], 2003. v. 1, p. 37–42.

KARNIK, N. N.; MENDEL, J. M. Introduction to type-2 fuzzy logic systems. In: 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence. [S.l.: s.n.], 1998. v. 2, p. 915–920 vol.2.

MENDEL, J. M.; JOHN, R. I.; LIU, F. Interval type-2 fuzzy logic systems made simple. IEEE Trans. Fuzzy Systems, v. 14, n. 6, p. 808–821, 2006.

GEHRKE, M.; WALKER, C.; WALKER, E. Some comments on interval valued fuzzy sets. International Journal of Intelligent Systems, v. 11, n. 10, p. 751–759, 1996.

KLEMENT, E.; MESIAR, R.; PAP, E. Triangular norms. position paper I: basic analytical and algebraic properties. Fuzzy Sets and Systems, v. 143, n. 1, p. 5–26, 2004.

WU, D.; NIE, M. Comparison and practical implementation of type-reduction algorithms for type-2 fuzzy sets and systems. In: FUZZ-IEEE. IEEE, 2011. p. 2131–2138. Disponível em:

ZAPATA, H. et al. Interval-valued implications and interval-valued strong equality index with admissible orders. International Journal of Approximate Reasoning, Elsevier, v. 88, p. 91–109, 2017.

BUSTINCE, H. et al. Generation of linear orders for intervals by means of aggregation functions. Fuzzy Sets and Systems, Elsevier, v. 220, p. 69–77, 2013.

XU, Z.; YAGER, R. R. Some geometric aggregation operators based on intuitionistic fuzzy sets. International journal of general systems, Taylor & Francis, v. 35, n. 4, p. 417–433, 2006.

MASOUMZADEH, S. S.; HLAVACS, H. Integrating VM selection criteria in distributed dynamic VM consolidation using Fuzzy Q-Learning. In: Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013). [S.l.: s.n.], 2013. p. 332–338.

PORTALURI, G. et al. Power consumption-aware virtual machine allocation in cloud data center. In: 2016 IEEE Globecom Workshops (GC Wkshps). [S.l.: s.n.], 2016. p. 1–6.

SINGH, S.; CHANA, I. Earth: Energy-aware autonomic resource scheduling in cloud computing. Journal of Intelligent & Fuzzy Systems, IOS Press, v. 30, n. 3, p. 1581–1600, 2016.

SALIMIAN, L.; ESFAHANI, F. S.; NADIMISHAHRAKI, M.-H. An adaptive fuzzy threshold-based approach for energy and performance efficient consolidation of virtual machines. Computing, v. 98, n. 6, p. 641–660, Jun 2016. Disponível em:

ARIANYAN, E.; TAHERI, H.; KHOSHDEL, V. Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers. Journal of Network and Computer Applications, v. 78, p. 43 – 61, 2017. Disponível em:

ALSADIE, D. et al. DTFA: A Dynamic Threshold-Based Fuzzy Approach for Power-Efficient VM Consolidation. In: 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA). [S.l.: s.n.], 2018. p. 1–9.

GLORENNEC, P. Y.; JOUFFE, L. Fuzzy Q-learning. In: Proceedings of 6th International Fuzzy Systems Conference. [S.l.: s.n.], 1997. v. 2, p. 659–662 vol.2.

ADAMI, D. et al. A fuzzy logic approach for resources allocation in cloud data center. In: 2015 IEEE Globecom Workshops (GC Wkshps). [S.l.: s.n.], 2015. p. 1–6.

BELOGLAZOV, A.; BUYYA, R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. : Pract. Exper., John Wiley and Sons Ltd., Chichester, UK, v. 24, n. 13, p. 1397–1420, set. 2012. Disponível em:

CINGOLANI, P.; ALCALÁ-FDEZ, J. jFuzzyLogic: a robust and flexible Fuzzy-Logic inference system language implementation. In: 2012 IEEE International Conference on Fuzzy Systems. [S.l.: s.n.], 2012. p. 1–8.

RADA-VILELA, J. The FuzzyLite Libraries for Fuzzy Logic Control. 2018. Disponível em:

MAMDANI, E. H. Application of fuzzy logic to approximate reasoning using linguistic synthesis. In: Proceedings of the Sixth International Symposium on Multiple-valued Logic. Los Alamitos, CA, USA: IEEE Computer Society Press, 1976. (MVL ’76), p. 196–202. Disponível em:

TAKAGI, T.; SUGENO, M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15, n. 1, p. 116–132, Jan 1985.

MINAS, L.; ELLISON, B. Energy Efficiency for Information Technology: How to Reduce Power Consumption in Servers and Data Centers. [S.l.]: Intel Press, 2009.

FAN, X.; WEBER, W.-D.; BARROSO, L. A. Power provisioning for a warehouse-sized computer. SIGARCH Comput. Archit. News, ACM, New York, NY, USA, v. 35, n. 2, p. 13–23, jun. 2007. Disponível em:

WAGNER, C. Juzzy - a java based toolkit for type-2 fuzzy logic. In: 2013 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ). [S.l.: s.n.], 2013. p. 45–52.

PARK, K.; PAI, V. S. Comon: A mostly-scalable monitoring system for planetlab. SIGOPS Oper. Syst. Rev., ACM, New York, NY, USA, v. 40, n. 1, p. 65–74, jan. 2006. Disponível em:




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.



Selected Papers - WEIT 2019