An improved rough set approach for optimal trust measure parameter selection in cloud environments
Abstract The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and trustworthy service providers based on their abilities and cloud users’ unique functional and non-functional quality-of-service (QoS) requir...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Nivethitha, Somu [verfasserIn] Gauthama Raman, M. R. [verfasserIn] Gireesha, Obulaporam [verfasserIn] Kannan, Krithivasan [verfasserIn] Shankar Sriram, V. S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
Trust measure parameters (TMPs) Binary fruit fly optimization (BFFO) |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 23(2019), 22 vom: 31. Jan., Seite 11979-11999 |
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| Übergeordnetes Werk: |
volume:23 ; year:2019 ; number:22 ; day:31 ; month:01 ; pages:11979-11999 |
| Links: |
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| DOI / URN: |
10.1007/s00500-018-03753-y |
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| Katalog-ID: |
SPR00650888X |
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| 520 | |a Abstract The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and trustworthy service providers based on their abilities and cloud users’ unique functional and non-functional quality-of-service (QoS) requirements. Further, the dynamic nature of the cloud ecosystem in terms of performance and new services increases the complexity of the cloud service selection problem. Trust-based service selection mechanisms which involve the intrinsic relations among the QoS parameters or trust measure parameters (TMPs) to evaluate the quality of the CSPs are the most preferred solution for the problem of cloud service selection. However, the accuracy of the trust-based service selection models and the CSP’s trust value relies on the optimality of the TMP subset obtained with respect to the service type. Hence, this work presents an efficient rough set theory-based hypergraph-binary fruit fly optimization (RST-HGBFFO), a cooperative bio-inspired technique to identify the optimal service-specific TMPs. Experiments on QWS dataset, Cloud Armor, and CISH—SASTRA trust feedback dataset reveal the predominance of RST-HGBFFO over the state-of-the-art feature selection techniques. The performance of RST-HGBFFO feature selection technique was validated using hypergraph-based computational model and WEKA tool in terms of reduct size, service ranking, classification accuracy, and time complexity. | ||
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| 650 | 4 | |a Binary fruit fly optimization (BFFO) |7 (dpeaa)DE-He213 | |
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10.1007/s00500-018-03753-y doi (DE-627)SPR00650888X (SPR)s00500-018-03753-y-e DE-627 ger DE-627 rakwb eng Nivethitha, Somu verfasserin aut An improved rough set approach for optimal trust measure parameter selection in cloud environments 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and trustworthy service providers based on their abilities and cloud users’ unique functional and non-functional quality-of-service (QoS) requirements. Further, the dynamic nature of the cloud ecosystem in terms of performance and new services increases the complexity of the cloud service selection problem. Trust-based service selection mechanisms which involve the intrinsic relations among the QoS parameters or trust measure parameters (TMPs) to evaluate the quality of the CSPs are the most preferred solution for the problem of cloud service selection. However, the accuracy of the trust-based service selection models and the CSP’s trust value relies on the optimality of the TMP subset obtained with respect to the service type. Hence, this work presents an efficient rough set theory-based hypergraph-binary fruit fly optimization (RST-HGBFFO), a cooperative bio-inspired technique to identify the optimal service-specific TMPs. Experiments on QWS dataset, Cloud Armor, and CISH—SASTRA trust feedback dataset reveal the predominance of RST-HGBFFO over the state-of-the-art feature selection techniques. The performance of RST-HGBFFO feature selection technique was validated using hypergraph-based computational model and WEKA tool in terms of reduct size, service ranking, classification accuracy, and time complexity. Trust measure parameters (TMPs) (dpeaa)DE-He213 Rough set theory (RST) (dpeaa)DE-He213 Hypergraph (dpeaa)DE-He213 Binary fruit fly optimization (BFFO) (dpeaa)DE-He213 Hypergraph-based computational model (HGCM) (dpeaa)DE-He213 Cloud service ranking (dpeaa)DE-He213 Gauthama Raman, M. R. verfasserin aut Gireesha, Obulaporam verfasserin aut Kannan, Krithivasan verfasserin aut Shankar Sriram, V. S. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 22 vom: 31. Jan., Seite 11979-11999 (DE-627)SPR006469531 nnns volume:23 year:2019 number:22 day:31 month:01 pages:11979-11999 https://dx.doi.org/10.1007/s00500-018-03753-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 22 31 01 11979-11999 |
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10.1007/s00500-018-03753-y doi (DE-627)SPR00650888X (SPR)s00500-018-03753-y-e DE-627 ger DE-627 rakwb eng Nivethitha, Somu verfasserin aut An improved rough set approach for optimal trust measure parameter selection in cloud environments 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and trustworthy service providers based on their abilities and cloud users’ unique functional and non-functional quality-of-service (QoS) requirements. Further, the dynamic nature of the cloud ecosystem in terms of performance and new services increases the complexity of the cloud service selection problem. Trust-based service selection mechanisms which involve the intrinsic relations among the QoS parameters or trust measure parameters (TMPs) to evaluate the quality of the CSPs are the most preferred solution for the problem of cloud service selection. However, the accuracy of the trust-based service selection models and the CSP’s trust value relies on the optimality of the TMP subset obtained with respect to the service type. Hence, this work presents an efficient rough set theory-based hypergraph-binary fruit fly optimization (RST-HGBFFO), a cooperative bio-inspired technique to identify the optimal service-specific TMPs. Experiments on QWS dataset, Cloud Armor, and CISH—SASTRA trust feedback dataset reveal the predominance of RST-HGBFFO over the state-of-the-art feature selection techniques. The performance of RST-HGBFFO feature selection technique was validated using hypergraph-based computational model and WEKA tool in terms of reduct size, service ranking, classification accuracy, and time complexity. Trust measure parameters (TMPs) (dpeaa)DE-He213 Rough set theory (RST) (dpeaa)DE-He213 Hypergraph (dpeaa)DE-He213 Binary fruit fly optimization (BFFO) (dpeaa)DE-He213 Hypergraph-based computational model (HGCM) (dpeaa)DE-He213 Cloud service ranking (dpeaa)DE-He213 Gauthama Raman, M. R. verfasserin aut Gireesha, Obulaporam verfasserin aut Kannan, Krithivasan verfasserin aut Shankar Sriram, V. S. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 22 vom: 31. Jan., Seite 11979-11999 (DE-627)SPR006469531 nnns volume:23 year:2019 number:22 day:31 month:01 pages:11979-11999 https://dx.doi.org/10.1007/s00500-018-03753-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 22 31 01 11979-11999 |
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10.1007/s00500-018-03753-y doi (DE-627)SPR00650888X (SPR)s00500-018-03753-y-e DE-627 ger DE-627 rakwb eng Nivethitha, Somu verfasserin aut An improved rough set approach for optimal trust measure parameter selection in cloud environments 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and trustworthy service providers based on their abilities and cloud users’ unique functional and non-functional quality-of-service (QoS) requirements. Further, the dynamic nature of the cloud ecosystem in terms of performance and new services increases the complexity of the cloud service selection problem. Trust-based service selection mechanisms which involve the intrinsic relations among the QoS parameters or trust measure parameters (TMPs) to evaluate the quality of the CSPs are the most preferred solution for the problem of cloud service selection. However, the accuracy of the trust-based service selection models and the CSP’s trust value relies on the optimality of the TMP subset obtained with respect to the service type. Hence, this work presents an efficient rough set theory-based hypergraph-binary fruit fly optimization (RST-HGBFFO), a cooperative bio-inspired technique to identify the optimal service-specific TMPs. Experiments on QWS dataset, Cloud Armor, and CISH—SASTRA trust feedback dataset reveal the predominance of RST-HGBFFO over the state-of-the-art feature selection techniques. The performance of RST-HGBFFO feature selection technique was validated using hypergraph-based computational model and WEKA tool in terms of reduct size, service ranking, classification accuracy, and time complexity. Trust measure parameters (TMPs) (dpeaa)DE-He213 Rough set theory (RST) (dpeaa)DE-He213 Hypergraph (dpeaa)DE-He213 Binary fruit fly optimization (BFFO) (dpeaa)DE-He213 Hypergraph-based computational model (HGCM) (dpeaa)DE-He213 Cloud service ranking (dpeaa)DE-He213 Gauthama Raman, M. R. verfasserin aut Gireesha, Obulaporam verfasserin aut Kannan, Krithivasan verfasserin aut Shankar Sriram, V. S. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 22 vom: 31. Jan., Seite 11979-11999 (DE-627)SPR006469531 nnns volume:23 year:2019 number:22 day:31 month:01 pages:11979-11999 https://dx.doi.org/10.1007/s00500-018-03753-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 22 31 01 11979-11999 |
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10.1007/s00500-018-03753-y doi (DE-627)SPR00650888X (SPR)s00500-018-03753-y-e DE-627 ger DE-627 rakwb eng Nivethitha, Somu verfasserin aut An improved rough set approach for optimal trust measure parameter selection in cloud environments 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and trustworthy service providers based on their abilities and cloud users’ unique functional and non-functional quality-of-service (QoS) requirements. Further, the dynamic nature of the cloud ecosystem in terms of performance and new services increases the complexity of the cloud service selection problem. Trust-based service selection mechanisms which involve the intrinsic relations among the QoS parameters or trust measure parameters (TMPs) to evaluate the quality of the CSPs are the most preferred solution for the problem of cloud service selection. However, the accuracy of the trust-based service selection models and the CSP’s trust value relies on the optimality of the TMP subset obtained with respect to the service type. Hence, this work presents an efficient rough set theory-based hypergraph-binary fruit fly optimization (RST-HGBFFO), a cooperative bio-inspired technique to identify the optimal service-specific TMPs. Experiments on QWS dataset, Cloud Armor, and CISH—SASTRA trust feedback dataset reveal the predominance of RST-HGBFFO over the state-of-the-art feature selection techniques. The performance of RST-HGBFFO feature selection technique was validated using hypergraph-based computational model and WEKA tool in terms of reduct size, service ranking, classification accuracy, and time complexity. Trust measure parameters (TMPs) (dpeaa)DE-He213 Rough set theory (RST) (dpeaa)DE-He213 Hypergraph (dpeaa)DE-He213 Binary fruit fly optimization (BFFO) (dpeaa)DE-He213 Hypergraph-based computational model (HGCM) (dpeaa)DE-He213 Cloud service ranking (dpeaa)DE-He213 Gauthama Raman, M. R. verfasserin aut Gireesha, Obulaporam verfasserin aut Kannan, Krithivasan verfasserin aut Shankar Sriram, V. S. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 22 vom: 31. Jan., Seite 11979-11999 (DE-627)SPR006469531 nnns volume:23 year:2019 number:22 day:31 month:01 pages:11979-11999 https://dx.doi.org/10.1007/s00500-018-03753-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 22 31 01 11979-11999 |
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10.1007/s00500-018-03753-y doi (DE-627)SPR00650888X (SPR)s00500-018-03753-y-e DE-627 ger DE-627 rakwb eng Nivethitha, Somu verfasserin aut An improved rough set approach for optimal trust measure parameter selection in cloud environments 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and trustworthy service providers based on their abilities and cloud users’ unique functional and non-functional quality-of-service (QoS) requirements. Further, the dynamic nature of the cloud ecosystem in terms of performance and new services increases the complexity of the cloud service selection problem. Trust-based service selection mechanisms which involve the intrinsic relations among the QoS parameters or trust measure parameters (TMPs) to evaluate the quality of the CSPs are the most preferred solution for the problem of cloud service selection. However, the accuracy of the trust-based service selection models and the CSP’s trust value relies on the optimality of the TMP subset obtained with respect to the service type. Hence, this work presents an efficient rough set theory-based hypergraph-binary fruit fly optimization (RST-HGBFFO), a cooperative bio-inspired technique to identify the optimal service-specific TMPs. Experiments on QWS dataset, Cloud Armor, and CISH—SASTRA trust feedback dataset reveal the predominance of RST-HGBFFO over the state-of-the-art feature selection techniques. The performance of RST-HGBFFO feature selection technique was validated using hypergraph-based computational model and WEKA tool in terms of reduct size, service ranking, classification accuracy, and time complexity. Trust measure parameters (TMPs) (dpeaa)DE-He213 Rough set theory (RST) (dpeaa)DE-He213 Hypergraph (dpeaa)DE-He213 Binary fruit fly optimization (BFFO) (dpeaa)DE-He213 Hypergraph-based computational model (HGCM) (dpeaa)DE-He213 Cloud service ranking (dpeaa)DE-He213 Gauthama Raman, M. R. verfasserin aut Gireesha, Obulaporam verfasserin aut Kannan, Krithivasan verfasserin aut Shankar Sriram, V. S. verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 22 vom: 31. Jan., Seite 11979-11999 (DE-627)SPR006469531 nnns volume:23 year:2019 number:22 day:31 month:01 pages:11979-11999 https://dx.doi.org/10.1007/s00500-018-03753-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 22 31 01 11979-11999 |
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An improved rough set approach for optimal trust measure parameter selection in cloud environments |
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An improved rough set approach for optimal trust measure parameter selection in cloud environments |
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Nivethitha, Somu |
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Soft Computing |
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Nivethitha, Somu Gauthama Raman, M. R. Gireesha, Obulaporam Kannan, Krithivasan Shankar Sriram, V. S. |
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Elektronische Aufsätze |
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Nivethitha, Somu |
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10.1007/s00500-018-03753-y |
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improved rough set approach for optimal trust measure parameter selection in cloud environments |
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An improved rough set approach for optimal trust measure parameter selection in cloud environments |
| abstract |
Abstract The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and trustworthy service providers based on their abilities and cloud users’ unique functional and non-functional quality-of-service (QoS) requirements. Further, the dynamic nature of the cloud ecosystem in terms of performance and new services increases the complexity of the cloud service selection problem. Trust-based service selection mechanisms which involve the intrinsic relations among the QoS parameters or trust measure parameters (TMPs) to evaluate the quality of the CSPs are the most preferred solution for the problem of cloud service selection. However, the accuracy of the trust-based service selection models and the CSP’s trust value relies on the optimality of the TMP subset obtained with respect to the service type. Hence, this work presents an efficient rough set theory-based hypergraph-binary fruit fly optimization (RST-HGBFFO), a cooperative bio-inspired technique to identify the optimal service-specific TMPs. Experiments on QWS dataset, Cloud Armor, and CISH—SASTRA trust feedback dataset reveal the predominance of RST-HGBFFO over the state-of-the-art feature selection techniques. The performance of RST-HGBFFO feature selection technique was validated using hypergraph-based computational model and WEKA tool in terms of reduct size, service ranking, classification accuracy, and time complexity. |
| abstractGer |
Abstract The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and trustworthy service providers based on their abilities and cloud users’ unique functional and non-functional quality-of-service (QoS) requirements. Further, the dynamic nature of the cloud ecosystem in terms of performance and new services increases the complexity of the cloud service selection problem. Trust-based service selection mechanisms which involve the intrinsic relations among the QoS parameters or trust measure parameters (TMPs) to evaluate the quality of the CSPs are the most preferred solution for the problem of cloud service selection. However, the accuracy of the trust-based service selection models and the CSP’s trust value relies on the optimality of the TMP subset obtained with respect to the service type. Hence, this work presents an efficient rough set theory-based hypergraph-binary fruit fly optimization (RST-HGBFFO), a cooperative bio-inspired technique to identify the optimal service-specific TMPs. Experiments on QWS dataset, Cloud Armor, and CISH—SASTRA trust feedback dataset reveal the predominance of RST-HGBFFO over the state-of-the-art feature selection techniques. The performance of RST-HGBFFO feature selection technique was validated using hypergraph-based computational model and WEKA tool in terms of reduct size, service ranking, classification accuracy, and time complexity. |
| abstract_unstemmed |
Abstract The existence of a multitude of cloud service providers (CSPs) for each service type increases the difficulty in the identification of appropriate and trustworthy service providers based on their abilities and cloud users’ unique functional and non-functional quality-of-service (QoS) requirements. Further, the dynamic nature of the cloud ecosystem in terms of performance and new services increases the complexity of the cloud service selection problem. Trust-based service selection mechanisms which involve the intrinsic relations among the QoS parameters or trust measure parameters (TMPs) to evaluate the quality of the CSPs are the most preferred solution for the problem of cloud service selection. However, the accuracy of the trust-based service selection models and the CSP’s trust value relies on the optimality of the TMP subset obtained with respect to the service type. Hence, this work presents an efficient rough set theory-based hypergraph-binary fruit fly optimization (RST-HGBFFO), a cooperative bio-inspired technique to identify the optimal service-specific TMPs. Experiments on QWS dataset, Cloud Armor, and CISH—SASTRA trust feedback dataset reveal the predominance of RST-HGBFFO over the state-of-the-art feature selection techniques. The performance of RST-HGBFFO feature selection technique was validated using hypergraph-based computational model and WEKA tool in terms of reduct size, service ranking, classification accuracy, and time complexity. |
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An improved rough set approach for optimal trust measure parameter selection in cloud environments |
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Gauthama Raman, M. R. Gireesha, Obulaporam Kannan, Krithivasan Shankar Sriram, V. S. |
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2024-07-03T23:19:45.420Z |
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