Genetic optimization of fuzzy membership functions for cloud resource provisioning

Amjad Ullah; Jingpeng Li; Amir Hussain; Yindong Shen

doi:10.1109/SSCI.2016.7850088

Genetic optimization of fuzzy membership functions for cloud resource provisioning

Amjad Ullah
, Jingpeng Li
, Amir Hussain
, Yindong Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

The successful usage of fuzzy systems can be seen in many application domains owing to their capabilities to model complex systems by exploiting knowledge of domain experts. Their accuracy and performance are, however, primarily dependent on the design of its membership functions and control rules. The commonly employed technique to design membership functions is to exploit the knowledge of domain experts. However, in certain application domains, the knowledge of domain experts are limited and therefore, cannot be relied upon. Alternatively, optimization techniques such as genetic algorithms are utilized to optimize the various design parameters of fuzzy systems. In this paper, we report a case study of optimizing the membership functions of a fuzzy system using genetic algorithm, which is an important part of our recently developed cloud elasticity framework. This work aims to improve the overall performance of the framework. Results obtained from this research work demonstrate performance improvement in comparison with our previous experimental settings.

Original language	English
Title of host publication	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509042401
DOIs	https://doi.org/10.1109/SSCI.2016.7850088
State	Published - 9 Feb 2017
Externally published	Yes
Event	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece Duration: 6 Dec 2016 → 9 Dec 2016

Publication series

Name	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Conference

Conference	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Country/Territory	Greece
City	Athens
Period	6/12/16 → 9/12/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Fuzzy logic
adaptive population size
cloud elasticity
cloud resource provisioning
fuzzy membership functions parameters tuning
genetic algorithms

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems and Management
Control and Optimization
Artificial Intelligence

Access to Document

10.1109/SSCI.2016.7850088

Cite this

Ullah, A., Li, J., Hussain, A., & Shen, Y. (2017). Genetic optimization of fuzzy membership functions for cloud resource provisioning. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 Article 7850088 (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2016.7850088

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title = "Genetic optimization of fuzzy membership functions for cloud resource provisioning",

abstract = "The successful usage of fuzzy systems can be seen in many application domains owing to their capabilities to model complex systems by exploiting knowledge of domain experts. Their accuracy and performance are, however, primarily dependent on the design of its membership functions and control rules. The commonly employed technique to design membership functions is to exploit the knowledge of domain experts. However, in certain application domains, the knowledge of domain experts are limited and therefore, cannot be relied upon. Alternatively, optimization techniques such as genetic algorithms are utilized to optimize the various design parameters of fuzzy systems. In this paper, we report a case study of optimizing the membership functions of a fuzzy system using genetic algorithm, which is an important part of our recently developed cloud elasticity framework. This work aims to improve the overall performance of the framework. Results obtained from this research work demonstrate performance improvement in comparison with our previous experimental settings.",

keywords = "Fuzzy logic, adaptive population size, cloud elasticity, cloud resource provisioning, fuzzy membership functions parameters tuning, genetic algorithms",

author = "Amjad Ullah and Jingpeng Li and Amir Hussain and Yindong Shen",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

year = "2017",

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doi = "10.1109/SSCI.2016.7850088",

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Ullah, A, Li, J, Hussain, A & Shen, Y 2017, Genetic optimization of fuzzy membership functions for cloud resource provisioning. in 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016., 7850088, 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, 6/12/16. https://doi.org/10.1109/SSCI.2016.7850088

Genetic optimization of fuzzy membership functions for cloud resource provisioning. / Ullah, Amjad; Li, Jingpeng; Hussain, Amir et al.
2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7850088 (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Genetic optimization of fuzzy membership functions for cloud resource provisioning

AU - Ullah, Amjad

AU - Li, Jingpeng

AU - Hussain, Amir

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N2 - The successful usage of fuzzy systems can be seen in many application domains owing to their capabilities to model complex systems by exploiting knowledge of domain experts. Their accuracy and performance are, however, primarily dependent on the design of its membership functions and control rules. The commonly employed technique to design membership functions is to exploit the knowledge of domain experts. However, in certain application domains, the knowledge of domain experts are limited and therefore, cannot be relied upon. Alternatively, optimization techniques such as genetic algorithms are utilized to optimize the various design parameters of fuzzy systems. In this paper, we report a case study of optimizing the membership functions of a fuzzy system using genetic algorithm, which is an important part of our recently developed cloud elasticity framework. This work aims to improve the overall performance of the framework. Results obtained from this research work demonstrate performance improvement in comparison with our previous experimental settings.

AB - The successful usage of fuzzy systems can be seen in many application domains owing to their capabilities to model complex systems by exploiting knowledge of domain experts. Their accuracy and performance are, however, primarily dependent on the design of its membership functions and control rules. The commonly employed technique to design membership functions is to exploit the knowledge of domain experts. However, in certain application domains, the knowledge of domain experts are limited and therefore, cannot be relied upon. Alternatively, optimization techniques such as genetic algorithms are utilized to optimize the various design parameters of fuzzy systems. In this paper, we report a case study of optimizing the membership functions of a fuzzy system using genetic algorithm, which is an important part of our recently developed cloud elasticity framework. This work aims to improve the overall performance of the framework. Results obtained from this research work demonstrate performance improvement in comparison with our previous experimental settings.

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KW - adaptive population size

KW - cloud elasticity

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KW - fuzzy membership functions parameters tuning

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AN - SCOPUS:85016025260

T3 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Genetic optimization of fuzzy membership functions for cloud resource provisioning

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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