A rough set-based hypergraph trust measure parameter selection technique for cloud service selection
Abstract Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Somu, Nivethitha [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
Cloud service providers (CSPs) |
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| Anmerkung: |
© Springer Science+Business Media New York 2017 |
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| Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Springer US, 1987, 73(2017), 10 vom: 12. Apr., Seite 4535-4559 |
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| Übergeordnetes Werk: |
volume:73 ; year:2017 ; number:10 ; day:12 ; month:04 ; pages:4535-4559 |
| Links: |
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| DOI / URN: |
10.1007/s11227-017-2032-8 |
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OLC2033953041 |
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| 520 | |a Abstract Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on their unique quality of service (QoS) requirements. A generic solution to the problem of cloud service selection can be formulated in terms of trust assessment. However, the accuracy of the trust value depends on the optimality of the service-specific trust measure parameters (TMPs) subset. This paper presents TrustCom—a novel trust assessment framework and rough set-based hypergraph technique (RSHT) for the identification of the optimal TMP subset. Experiments using Cloud Armor and synthetic trust feedback datasets show the prominence of RSHT over the existing feature selection techniques. The performance of RSHT was analyzed using Weka tool and hypergraph-based computational model with respect to the reduct size, time complexity and service ranking. | ||
| 650 | 4 | |a Cloud service providers (CSPs) | |
| 650 | 4 | |a Cloud users (CUs) | |
| 650 | 4 | |a Trust measure parameters (TMPs) | |
| 650 | 4 | |a Rough set theory (RST) | |
| 650 | 4 | |a Hypergraph | |
| 650 | 4 | |a Hypergraph-based computational model (HGCM) | |
| 700 | 1 | |a Kirthivasan, Kannan |4 aut | |
| 700 | 1 | |a Shankar Sriram, V. S. |0 (orcid)0000-0001-7870-7944 |4 aut | |
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10.1007/s11227-017-2032-8 doi (DE-627)OLC2033953041 (DE-He213)s11227-017-2032-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Somu, Nivethitha verfasserin aut A rough set-based hypergraph trust measure parameter selection technique for cloud service selection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on their unique quality of service (QoS) requirements. A generic solution to the problem of cloud service selection can be formulated in terms of trust assessment. However, the accuracy of the trust value depends on the optimality of the service-specific trust measure parameters (TMPs) subset. This paper presents TrustCom—a novel trust assessment framework and rough set-based hypergraph technique (RSHT) for the identification of the optimal TMP subset. Experiments using Cloud Armor and synthetic trust feedback datasets show the prominence of RSHT over the existing feature selection techniques. The performance of RSHT was analyzed using Weka tool and hypergraph-based computational model with respect to the reduct size, time complexity and service ranking. Cloud service providers (CSPs) Cloud users (CUs) Trust measure parameters (TMPs) Rough set theory (RST) Hypergraph Hypergraph-based computational model (HGCM) Kirthivasan, Kannan aut Shankar Sriram, V. S. (orcid)0000-0001-7870-7944 aut Enthalten in The journal of supercomputing Springer US, 1987 73(2017), 10 vom: 12. Apr., Seite 4535-4559 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:73 year:2017 number:10 day:12 month:04 pages:4535-4559 https://doi.org/10.1007/s11227-017-2032-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 73 2017 10 12 04 4535-4559 |
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10.1007/s11227-017-2032-8 doi (DE-627)OLC2033953041 (DE-He213)s11227-017-2032-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Somu, Nivethitha verfasserin aut A rough set-based hypergraph trust measure parameter selection technique for cloud service selection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on their unique quality of service (QoS) requirements. A generic solution to the problem of cloud service selection can be formulated in terms of trust assessment. However, the accuracy of the trust value depends on the optimality of the service-specific trust measure parameters (TMPs) subset. This paper presents TrustCom—a novel trust assessment framework and rough set-based hypergraph technique (RSHT) for the identification of the optimal TMP subset. Experiments using Cloud Armor and synthetic trust feedback datasets show the prominence of RSHT over the existing feature selection techniques. The performance of RSHT was analyzed using Weka tool and hypergraph-based computational model with respect to the reduct size, time complexity and service ranking. Cloud service providers (CSPs) Cloud users (CUs) Trust measure parameters (TMPs) Rough set theory (RST) Hypergraph Hypergraph-based computational model (HGCM) Kirthivasan, Kannan aut Shankar Sriram, V. S. (orcid)0000-0001-7870-7944 aut Enthalten in The journal of supercomputing Springer US, 1987 73(2017), 10 vom: 12. Apr., Seite 4535-4559 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:73 year:2017 number:10 day:12 month:04 pages:4535-4559 https://doi.org/10.1007/s11227-017-2032-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 73 2017 10 12 04 4535-4559 |
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10.1007/s11227-017-2032-8 doi (DE-627)OLC2033953041 (DE-He213)s11227-017-2032-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Somu, Nivethitha verfasserin aut A rough set-based hypergraph trust measure parameter selection technique for cloud service selection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on their unique quality of service (QoS) requirements. A generic solution to the problem of cloud service selection can be formulated in terms of trust assessment. However, the accuracy of the trust value depends on the optimality of the service-specific trust measure parameters (TMPs) subset. This paper presents TrustCom—a novel trust assessment framework and rough set-based hypergraph technique (RSHT) for the identification of the optimal TMP subset. Experiments using Cloud Armor and synthetic trust feedback datasets show the prominence of RSHT over the existing feature selection techniques. The performance of RSHT was analyzed using Weka tool and hypergraph-based computational model with respect to the reduct size, time complexity and service ranking. Cloud service providers (CSPs) Cloud users (CUs) Trust measure parameters (TMPs) Rough set theory (RST) Hypergraph Hypergraph-based computational model (HGCM) Kirthivasan, Kannan aut Shankar Sriram, V. S. (orcid)0000-0001-7870-7944 aut Enthalten in The journal of supercomputing Springer US, 1987 73(2017), 10 vom: 12. Apr., Seite 4535-4559 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:73 year:2017 number:10 day:12 month:04 pages:4535-4559 https://doi.org/10.1007/s11227-017-2032-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 73 2017 10 12 04 4535-4559 |
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10.1007/s11227-017-2032-8 doi (DE-627)OLC2033953041 (DE-He213)s11227-017-2032-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Somu, Nivethitha verfasserin aut A rough set-based hypergraph trust measure parameter selection technique for cloud service selection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on their unique quality of service (QoS) requirements. A generic solution to the problem of cloud service selection can be formulated in terms of trust assessment. However, the accuracy of the trust value depends on the optimality of the service-specific trust measure parameters (TMPs) subset. This paper presents TrustCom—a novel trust assessment framework and rough set-based hypergraph technique (RSHT) for the identification of the optimal TMP subset. Experiments using Cloud Armor and synthetic trust feedback datasets show the prominence of RSHT over the existing feature selection techniques. The performance of RSHT was analyzed using Weka tool and hypergraph-based computational model with respect to the reduct size, time complexity and service ranking. Cloud service providers (CSPs) Cloud users (CUs) Trust measure parameters (TMPs) Rough set theory (RST) Hypergraph Hypergraph-based computational model (HGCM) Kirthivasan, Kannan aut Shankar Sriram, V. S. (orcid)0000-0001-7870-7944 aut Enthalten in The journal of supercomputing Springer US, 1987 73(2017), 10 vom: 12. Apr., Seite 4535-4559 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:73 year:2017 number:10 day:12 month:04 pages:4535-4559 https://doi.org/10.1007/s11227-017-2032-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 73 2017 10 12 04 4535-4559 |
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A rough set-based hypergraph trust measure parameter selection technique for cloud service selection |
| abstract |
Abstract Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on their unique quality of service (QoS) requirements. A generic solution to the problem of cloud service selection can be formulated in terms of trust assessment. However, the accuracy of the trust value depends on the optimality of the service-specific trust measure parameters (TMPs) subset. This paper presents TrustCom—a novel trust assessment framework and rough set-based hypergraph technique (RSHT) for the identification of the optimal TMP subset. Experiments using Cloud Armor and synthetic trust feedback datasets show the prominence of RSHT over the existing feature selection techniques. The performance of RSHT was analyzed using Weka tool and hypergraph-based computational model with respect to the reduct size, time complexity and service ranking. © Springer Science+Business Media New York 2017 |
| abstractGer |
Abstract Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on their unique quality of service (QoS) requirements. A generic solution to the problem of cloud service selection can be formulated in terms of trust assessment. However, the accuracy of the trust value depends on the optimality of the service-specific trust measure parameters (TMPs) subset. This paper presents TrustCom—a novel trust assessment framework and rough set-based hypergraph technique (RSHT) for the identification of the optimal TMP subset. Experiments using Cloud Armor and synthetic trust feedback datasets show the prominence of RSHT over the existing feature selection techniques. The performance of RSHT was analyzed using Weka tool and hypergraph-based computational model with respect to the reduct size, time complexity and service ranking. © Springer Science+Business Media New York 2017 |
| abstract_unstemmed |
Abstract Selection of trustworthy cloud services has been a major research challenge in cloud computing, due to the proliferation of numerous cloud service providers (CSPs) along every dimension of computing. This scenario makes it hard for the cloud users to identify an appropriate CSP based on their unique quality of service (QoS) requirements. A generic solution to the problem of cloud service selection can be formulated in terms of trust assessment. However, the accuracy of the trust value depends on the optimality of the service-specific trust measure parameters (TMPs) subset. This paper presents TrustCom—a novel trust assessment framework and rough set-based hypergraph technique (RSHT) for the identification of the optimal TMP subset. Experiments using Cloud Armor and synthetic trust feedback datasets show the prominence of RSHT over the existing feature selection techniques. The performance of RSHT was analyzed using Weka tool and hypergraph-based computational model with respect to the reduct size, time complexity and service ranking. © Springer Science+Business Media New York 2017 |
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10 |
| title_short |
A rough set-based hypergraph trust measure parameter selection technique for cloud service selection |
| url |
https://doi.org/10.1007/s11227-017-2032-8 |
| remote_bool |
false |
| author2 |
Kirthivasan, Kannan Shankar Sriram, V. S. |
| author2Str |
Kirthivasan, Kannan Shankar Sriram, V. S. |
| ppnlink |
13046466X |
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| hochschulschrift_bool |
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| doi_str |
10.1007/s11227-017-2032-8 |
| up_date |
2024-07-03T19:03:13.385Z |
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|
| score |
7.401513 |