Int-fGrid: BoT Tasks Scheduling Exploring Fuzzy Type-2 in Computational Grids

Moura, Bruno; Schneider, Guilherme Bayer; Yamin, Adenauer; Pilla, Maurı́cio L.; Reiser, Renata

doi:10.1109/fuzz-ieee.2018.8491673

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…Considerando este contexto e levando em conta alguns trabalhos como os projetos [1] e [7], que utilizam Lógica Fuzzy (Fuzzy Logic -FL) para modelagem de incerteza no LB na CN, este trabalho discute Int-FLBCC (Interval Fuzzy Load Balancing for Cloud Computing). O desenvolvimento desta proposta estende a abordagem proposta em [8], contribuindo para a consolidac ¸ão dinâmica de VM empregando a Lógica Fuzzy Tipo-2 (Type-2 Fuzzy Logic -T2FL). Particularmente, interpreta-se nesta modelagem a incerteza e a imprecisão inerentes às variáveis da CN, neste caso PC, CC e RAM, buscando ainda o equilíbrio entre a eficiência energética e SLA.…”

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

Schneider

Moura

Yamin

et al. 2020

RITA

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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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Section: Introduc ¸ãOunclassified

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

Schneider

Moura

Yamin

et al. 2020

RITA

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“…(ii) are the load balancers equipped with consistent techniques for the treatment of the uncertainties related to cloud computing environments? and (iii) do the users have the necessary knowledge about the computational and communication demands of their applications?This proposal extends the approaches in [16,15], modeling the uncertain variables generating CP, CC, and RAM in Load Balancing (LB) considering Type-2 Fuzzy Logic (T2FL) which can preserve the balance between energy efficiency and manage resources while ensuring QoS through SLAs for such cloud-based services.The paper is structured as follows. Related works are presented in section 2.…”

mentioning

confidence: 99%

“…This proposal extends the approaches in [16,15], modeling the uncertain variables generating CP, CC, and RAM in Load Balancing (LB) considering Type-2 Fuzzy Logic (T2FL) which can preserve the balance between energy efficiency and manage resources while ensuring QoS through SLAs for such cloud-based services.…”

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confidence: 99%

Type-2 Fuzzy Logic Approach for Overloaded Hosts in Consolidation of Virtual Machines in Cloud Computing

Moura¹,

Schneider²,

Yamin³

et al. 2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing. Determining when it is best to relocate VMs from an overloaded host is one aspect of dynamic VM consolidation that directly influences the resource utilization and Quality of Service (QoS) offered by the system. This paper presents a new proposal with a Type-2 Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. Results in a simulated cloud computing environment show improvements in energy efficiency of 15.64% in the Median Absolute Deviation (MAD), 15.43% in the Inter Quartile Range (IQR), and 9.19% in the Local Regression (LR).The following research questions stand out: (i) do load balancers consider the uncertainties associated with factors such as CP, CC, and RAM? (ii) are the load balancers equipped with consistent techniques for the treatment of the uncertainties related to cloud computing environments? and (iii) do the users have the necessary knowledge about the computational and communication demands of their applications?This proposal extends the approaches in [16,15], modeling the uncertain variables generating CP, CC, and RAM in Load Balancing (LB) considering Type-2 Fuzzy Logic (T2FL) which can preserve the balance between energy efficiency and manage resources while ensuring QoS through SLAs for such cloud-based services.The paper is structured as follows. Related works are presented in section 2. Section 3 introduces basic concepts of Type-2 Fuzzy Logic. In section 4, details of the Int-FLBCC component and its conception are discussed, including database, fuzzification, rule base, inference, and defuzzification. Section 5 describes the experimental evaluation. Finally, section 6 presents conclusions and future work.

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