Non-dominated Sorting Genetic Algorithm (NSGA-III) for effective resource allocation in cloud
Abstract Resource management system helps the enterprises to coordinate the IT resources in connection to the action performed by the key players such as cloud customers and service providers. Present day cloud resource and service providers use a heterogeneous allocation strategy for resources allo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Miriam, A. Jemshia [verfasserIn] Saminathan, R. [verfasserIn] Chakaravarthi, S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Evolutionary intelligence - Berlin : Springer, 2008, 14(2020), 2 vom: 12. Juni, Seite 759-765 |
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| Übergeordnetes Werk: |
volume:14 ; year:2020 ; number:2 ; day:12 ; month:06 ; pages:759-765 |
| Links: |
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| DOI / URN: |
10.1007/s12065-020-00436-2 |
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| Katalog-ID: |
SPR044195575 |
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| 245 | 1 | 0 | |a Non-dominated Sorting Genetic Algorithm (NSGA-III) for effective resource allocation in cloud |
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| 520 | |a Abstract Resource management system helps the enterprises to coordinate the IT resources in connection to the action performed by the key players such as cloud customers and service providers. Present day cloud resource and service providers use a heterogeneous allocation strategy for resources allocation across various geographical locations. Further, these allocations are completely in need of secure transactions, effective scheduling and dynamic resource allocation strategies. To overcome the above mentioned issues, this paper proposes a novel resource allocation framework for the cloud service providers to schedule and effective resource allocation. The key idea of the proposed resource allocation scheme is to utilize Non dominated Sorting Genetic Algorithm (NSGA-III) to effectively allocate resources. Furthermore, the proposed NSGA-III is modified to support any interim data sources (any middle wares). The proposed model is experimentally validated in the test bed with multi-node Hadoop cluster. The experimental results confirm that the proposed model outperforms the existing state of the art models such as Lion optimization, Traditional ACO and Particle based Kernel function algorithms with more than 95% in accuracy. | ||
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| 650 | 4 | |a Ant colony optimization algorithm |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cloud resource allocation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Saminathan, R. |e verfasserin |4 aut | |
| 700 | 1 | |a Chakaravarthi, S. |e verfasserin |4 aut | |
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10.1007/s12065-020-00436-2 doi (DE-627)SPR044195575 (SPR)s12065-020-00436-2-e DE-627 ger DE-627 rakwb eng 004 ASE Miriam, A. Jemshia verfasserin aut Non-dominated Sorting Genetic Algorithm (NSGA-III) for effective resource allocation in cloud 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Resource management system helps the enterprises to coordinate the IT resources in connection to the action performed by the key players such as cloud customers and service providers. Present day cloud resource and service providers use a heterogeneous allocation strategy for resources allocation across various geographical locations. Further, these allocations are completely in need of secure transactions, effective scheduling and dynamic resource allocation strategies. To overcome the above mentioned issues, this paper proposes a novel resource allocation framework for the cloud service providers to schedule and effective resource allocation. The key idea of the proposed resource allocation scheme is to utilize Non dominated Sorting Genetic Algorithm (NSGA-III) to effectively allocate resources. Furthermore, the proposed NSGA-III is modified to support any interim data sources (any middle wares). The proposed model is experimentally validated in the test bed with multi-node Hadoop cluster. The experimental results confirm that the proposed model outperforms the existing state of the art models such as Lion optimization, Traditional ACO and Particle based Kernel function algorithms with more than 95% in accuracy. NSGA (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 NSGA-III (dpeaa)DE-He213 Ant colony optimization (dpeaa)DE-He213 Ant colony optimization algorithm (dpeaa)DE-He213 Cloud resource allocation (dpeaa)DE-He213 Saminathan, R. verfasserin aut Chakaravarthi, S. verfasserin aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 14(2020), 2 vom: 12. Juni, Seite 759-765 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:14 year:2020 number:2 day:12 month:06 pages:759-765 https://dx.doi.org/10.1007/s12065-020-00436-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 2 12 06 759-765 |
| spelling |
10.1007/s12065-020-00436-2 doi (DE-627)SPR044195575 (SPR)s12065-020-00436-2-e DE-627 ger DE-627 rakwb eng 004 ASE Miriam, A. Jemshia verfasserin aut Non-dominated Sorting Genetic Algorithm (NSGA-III) for effective resource allocation in cloud 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Resource management system helps the enterprises to coordinate the IT resources in connection to the action performed by the key players such as cloud customers and service providers. Present day cloud resource and service providers use a heterogeneous allocation strategy for resources allocation across various geographical locations. Further, these allocations are completely in need of secure transactions, effective scheduling and dynamic resource allocation strategies. To overcome the above mentioned issues, this paper proposes a novel resource allocation framework for the cloud service providers to schedule and effective resource allocation. The key idea of the proposed resource allocation scheme is to utilize Non dominated Sorting Genetic Algorithm (NSGA-III) to effectively allocate resources. Furthermore, the proposed NSGA-III is modified to support any interim data sources (any middle wares). The proposed model is experimentally validated in the test bed with multi-node Hadoop cluster. The experimental results confirm that the proposed model outperforms the existing state of the art models such as Lion optimization, Traditional ACO and Particle based Kernel function algorithms with more than 95% in accuracy. NSGA (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 NSGA-III (dpeaa)DE-He213 Ant colony optimization (dpeaa)DE-He213 Ant colony optimization algorithm (dpeaa)DE-He213 Cloud resource allocation (dpeaa)DE-He213 Saminathan, R. verfasserin aut Chakaravarthi, S. verfasserin aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 14(2020), 2 vom: 12. Juni, Seite 759-765 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:14 year:2020 number:2 day:12 month:06 pages:759-765 https://dx.doi.org/10.1007/s12065-020-00436-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 2 12 06 759-765 |
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10.1007/s12065-020-00436-2 doi (DE-627)SPR044195575 (SPR)s12065-020-00436-2-e DE-627 ger DE-627 rakwb eng 004 ASE Miriam, A. Jemshia verfasserin aut Non-dominated Sorting Genetic Algorithm (NSGA-III) for effective resource allocation in cloud 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Resource management system helps the enterprises to coordinate the IT resources in connection to the action performed by the key players such as cloud customers and service providers. Present day cloud resource and service providers use a heterogeneous allocation strategy for resources allocation across various geographical locations. Further, these allocations are completely in need of secure transactions, effective scheduling and dynamic resource allocation strategies. To overcome the above mentioned issues, this paper proposes a novel resource allocation framework for the cloud service providers to schedule and effective resource allocation. The key idea of the proposed resource allocation scheme is to utilize Non dominated Sorting Genetic Algorithm (NSGA-III) to effectively allocate resources. Furthermore, the proposed NSGA-III is modified to support any interim data sources (any middle wares). The proposed model is experimentally validated in the test bed with multi-node Hadoop cluster. The experimental results confirm that the proposed model outperforms the existing state of the art models such as Lion optimization, Traditional ACO and Particle based Kernel function algorithms with more than 95% in accuracy. NSGA (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 NSGA-III (dpeaa)DE-He213 Ant colony optimization (dpeaa)DE-He213 Ant colony optimization algorithm (dpeaa)DE-He213 Cloud resource allocation (dpeaa)DE-He213 Saminathan, R. verfasserin aut Chakaravarthi, S. verfasserin aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 14(2020), 2 vom: 12. Juni, Seite 759-765 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:14 year:2020 number:2 day:12 month:06 pages:759-765 https://dx.doi.org/10.1007/s12065-020-00436-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 2 12 06 759-765 |
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10.1007/s12065-020-00436-2 doi (DE-627)SPR044195575 (SPR)s12065-020-00436-2-e DE-627 ger DE-627 rakwb eng 004 ASE Miriam, A. Jemshia verfasserin aut Non-dominated Sorting Genetic Algorithm (NSGA-III) for effective resource allocation in cloud 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Resource management system helps the enterprises to coordinate the IT resources in connection to the action performed by the key players such as cloud customers and service providers. Present day cloud resource and service providers use a heterogeneous allocation strategy for resources allocation across various geographical locations. Further, these allocations are completely in need of secure transactions, effective scheduling and dynamic resource allocation strategies. To overcome the above mentioned issues, this paper proposes a novel resource allocation framework for the cloud service providers to schedule and effective resource allocation. The key idea of the proposed resource allocation scheme is to utilize Non dominated Sorting Genetic Algorithm (NSGA-III) to effectively allocate resources. Furthermore, the proposed NSGA-III is modified to support any interim data sources (any middle wares). The proposed model is experimentally validated in the test bed with multi-node Hadoop cluster. The experimental results confirm that the proposed model outperforms the existing state of the art models such as Lion optimization, Traditional ACO and Particle based Kernel function algorithms with more than 95% in accuracy. NSGA (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 NSGA-III (dpeaa)DE-He213 Ant colony optimization (dpeaa)DE-He213 Ant colony optimization algorithm (dpeaa)DE-He213 Cloud resource allocation (dpeaa)DE-He213 Saminathan, R. verfasserin aut Chakaravarthi, S. verfasserin aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 14(2020), 2 vom: 12. Juni, Seite 759-765 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:14 year:2020 number:2 day:12 month:06 pages:759-765 https://dx.doi.org/10.1007/s12065-020-00436-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 2 12 06 759-765 |
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10.1007/s12065-020-00436-2 doi (DE-627)SPR044195575 (SPR)s12065-020-00436-2-e DE-627 ger DE-627 rakwb eng 004 ASE Miriam, A. Jemshia verfasserin aut Non-dominated Sorting Genetic Algorithm (NSGA-III) for effective resource allocation in cloud 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Resource management system helps the enterprises to coordinate the IT resources in connection to the action performed by the key players such as cloud customers and service providers. Present day cloud resource and service providers use a heterogeneous allocation strategy for resources allocation across various geographical locations. Further, these allocations are completely in need of secure transactions, effective scheduling and dynamic resource allocation strategies. To overcome the above mentioned issues, this paper proposes a novel resource allocation framework for the cloud service providers to schedule and effective resource allocation. The key idea of the proposed resource allocation scheme is to utilize Non dominated Sorting Genetic Algorithm (NSGA-III) to effectively allocate resources. Furthermore, the proposed NSGA-III is modified to support any interim data sources (any middle wares). The proposed model is experimentally validated in the test bed with multi-node Hadoop cluster. The experimental results confirm that the proposed model outperforms the existing state of the art models such as Lion optimization, Traditional ACO and Particle based Kernel function algorithms with more than 95% in accuracy. NSGA (dpeaa)DE-He213 NSGA-II (dpeaa)DE-He213 NSGA-III (dpeaa)DE-He213 Ant colony optimization (dpeaa)DE-He213 Ant colony optimization algorithm (dpeaa)DE-He213 Cloud resource allocation (dpeaa)DE-He213 Saminathan, R. verfasserin aut Chakaravarthi, S. verfasserin aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 14(2020), 2 vom: 12. Juni, Seite 759-765 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:14 year:2020 number:2 day:12 month:06 pages:759-765 https://dx.doi.org/10.1007/s12065-020-00436-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2020 2 12 06 759-765 |
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non-dominated sorting genetic algorithm (nsga-iii) for effective resource allocation in cloud |
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Non-dominated Sorting Genetic Algorithm (NSGA-III) for effective resource allocation in cloud |
| abstract |
Abstract Resource management system helps the enterprises to coordinate the IT resources in connection to the action performed by the key players such as cloud customers and service providers. Present day cloud resource and service providers use a heterogeneous allocation strategy for resources allocation across various geographical locations. Further, these allocations are completely in need of secure transactions, effective scheduling and dynamic resource allocation strategies. To overcome the above mentioned issues, this paper proposes a novel resource allocation framework for the cloud service providers to schedule and effective resource allocation. The key idea of the proposed resource allocation scheme is to utilize Non dominated Sorting Genetic Algorithm (NSGA-III) to effectively allocate resources. Furthermore, the proposed NSGA-III is modified to support any interim data sources (any middle wares). The proposed model is experimentally validated in the test bed with multi-node Hadoop cluster. The experimental results confirm that the proposed model outperforms the existing state of the art models such as Lion optimization, Traditional ACO and Particle based Kernel function algorithms with more than 95% in accuracy. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
| abstractGer |
Abstract Resource management system helps the enterprises to coordinate the IT resources in connection to the action performed by the key players such as cloud customers and service providers. Present day cloud resource and service providers use a heterogeneous allocation strategy for resources allocation across various geographical locations. Further, these allocations are completely in need of secure transactions, effective scheduling and dynamic resource allocation strategies. To overcome the above mentioned issues, this paper proposes a novel resource allocation framework for the cloud service providers to schedule and effective resource allocation. The key idea of the proposed resource allocation scheme is to utilize Non dominated Sorting Genetic Algorithm (NSGA-III) to effectively allocate resources. Furthermore, the proposed NSGA-III is modified to support any interim data sources (any middle wares). The proposed model is experimentally validated in the test bed with multi-node Hadoop cluster. The experimental results confirm that the proposed model outperforms the existing state of the art models such as Lion optimization, Traditional ACO and Particle based Kernel function algorithms with more than 95% in accuracy. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Resource management system helps the enterprises to coordinate the IT resources in connection to the action performed by the key players such as cloud customers and service providers. Present day cloud resource and service providers use a heterogeneous allocation strategy for resources allocation across various geographical locations. Further, these allocations are completely in need of secure transactions, effective scheduling and dynamic resource allocation strategies. To overcome the above mentioned issues, this paper proposes a novel resource allocation framework for the cloud service providers to schedule and effective resource allocation. The key idea of the proposed resource allocation scheme is to utilize Non dominated Sorting Genetic Algorithm (NSGA-III) to effectively allocate resources. Furthermore, the proposed NSGA-III is modified to support any interim data sources (any middle wares). The proposed model is experimentally validated in the test bed with multi-node Hadoop cluster. The experimental results confirm that the proposed model outperforms the existing state of the art models such as Lion optimization, Traditional ACO and Particle based Kernel function algorithms with more than 95% in accuracy. © Springer-Verlag GmbH Germany, part of Springer Nature 2020 |
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Non-dominated Sorting Genetic Algorithm (NSGA-III) for effective resource allocation in cloud |
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Saminathan, R. Chakaravarthi, S. |
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10.1007/s12065-020-00436-2 |
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| score |
7.3983126 |