A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback
Abstract With the rapid development of cloud computing services, it is highly essential to ensure the quality of service offered by the service providers. Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large nu...
Ausführliche Beschreibung
Autor*in: |
Nagarajan, Rajganesh [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2017 |
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Schlagwörter: |
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Anmerkung: |
© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany 2017 |
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Übergeordnetes Werk: |
Enthalten in: International journal of fuzzy systems - Taibei : Association, 2006, 20(2017), 1 vom: 29. Juni, Seite 339-347 |
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Übergeordnetes Werk: |
volume:20 ; year:2017 ; number:1 ; day:29 ; month:06 ; pages:339-347 |
Links: |
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DOI / URN: |
10.1007/s40815-017-0347-5 |
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Katalog-ID: |
SPR037857770 |
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520 | |a Abstract With the rapid development of cloud computing services, it is highly essential to ensure the quality of service offered by the service providers. Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large number of cloud users are involved. To tackle the problem, we have advocated a big data processing framework for evaluating the trust level of availed services. An intelligent cloud broker with the incorporation of MapReduce framework has been put forth for the effective preprocessing of cloud users’ feedback. Besides, the broker performs the fuzzy inference system and a decision-making process for evaluating the trust level of services on the basis of processed feedback. Experimental results show that our proposed framework scores better results in terms of both trust level identification and execution efficiency. | ||
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650 | 4 | |a Decision making |7 (dpeaa)DE-He213 | |
700 | 1 | |a Thirunavukarasu, Ramkumar |4 aut | |
700 | 1 | |a Shanmugam, Selvamuthukumaran |4 aut | |
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10.1007/s40815-017-0347-5 doi (DE-627)SPR037857770 (SPR)s40815-017-0347-5-e DE-627 ger DE-627 rakwb eng Nagarajan, Rajganesh verfasserin aut A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany 2017 Abstract With the rapid development of cloud computing services, it is highly essential to ensure the quality of service offered by the service providers. Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large number of cloud users are involved. To tackle the problem, we have advocated a big data processing framework for evaluating the trust level of availed services. An intelligent cloud broker with the incorporation of MapReduce framework has been put forth for the effective preprocessing of cloud users’ feedback. Besides, the broker performs the fuzzy inference system and a decision-making process for evaluating the trust level of services on the basis of processed feedback. Experimental results show that our proposed framework scores better results in terms of both trust level identification and execution efficiency. Cloud service (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Cloud broker (dpeaa)DE-He213 Decision making (dpeaa)DE-He213 Thirunavukarasu, Ramkumar aut Shanmugam, Selvamuthukumaran aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2017), 1 vom: 29. Juni, Seite 339-347 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2017 number:1 day:29 month:06 pages:339-347 https://dx.doi.org/10.1007/s40815-017-0347-5 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2017 1 29 06 339-347 |
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10.1007/s40815-017-0347-5 doi (DE-627)SPR037857770 (SPR)s40815-017-0347-5-e DE-627 ger DE-627 rakwb eng Nagarajan, Rajganesh verfasserin aut A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany 2017 Abstract With the rapid development of cloud computing services, it is highly essential to ensure the quality of service offered by the service providers. Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large number of cloud users are involved. To tackle the problem, we have advocated a big data processing framework for evaluating the trust level of availed services. An intelligent cloud broker with the incorporation of MapReduce framework has been put forth for the effective preprocessing of cloud users’ feedback. Besides, the broker performs the fuzzy inference system and a decision-making process for evaluating the trust level of services on the basis of processed feedback. Experimental results show that our proposed framework scores better results in terms of both trust level identification and execution efficiency. Cloud service (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Cloud broker (dpeaa)DE-He213 Decision making (dpeaa)DE-He213 Thirunavukarasu, Ramkumar aut Shanmugam, Selvamuthukumaran aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2017), 1 vom: 29. Juni, Seite 339-347 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2017 number:1 day:29 month:06 pages:339-347 https://dx.doi.org/10.1007/s40815-017-0347-5 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2017 1 29 06 339-347 |
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10.1007/s40815-017-0347-5 doi (DE-627)SPR037857770 (SPR)s40815-017-0347-5-e DE-627 ger DE-627 rakwb eng Nagarajan, Rajganesh verfasserin aut A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany 2017 Abstract With the rapid development of cloud computing services, it is highly essential to ensure the quality of service offered by the service providers. Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large number of cloud users are involved. To tackle the problem, we have advocated a big data processing framework for evaluating the trust level of availed services. An intelligent cloud broker with the incorporation of MapReduce framework has been put forth for the effective preprocessing of cloud users’ feedback. Besides, the broker performs the fuzzy inference system and a decision-making process for evaluating the trust level of services on the basis of processed feedback. Experimental results show that our proposed framework scores better results in terms of both trust level identification and execution efficiency. Cloud service (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Cloud broker (dpeaa)DE-He213 Decision making (dpeaa)DE-He213 Thirunavukarasu, Ramkumar aut Shanmugam, Selvamuthukumaran aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2017), 1 vom: 29. Juni, Seite 339-347 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2017 number:1 day:29 month:06 pages:339-347 https://dx.doi.org/10.1007/s40815-017-0347-5 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2017 1 29 06 339-347 |
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10.1007/s40815-017-0347-5 doi (DE-627)SPR037857770 (SPR)s40815-017-0347-5-e DE-627 ger DE-627 rakwb eng Nagarajan, Rajganesh verfasserin aut A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany 2017 Abstract With the rapid development of cloud computing services, it is highly essential to ensure the quality of service offered by the service providers. Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large number of cloud users are involved. To tackle the problem, we have advocated a big data processing framework for evaluating the trust level of availed services. An intelligent cloud broker with the incorporation of MapReduce framework has been put forth for the effective preprocessing of cloud users’ feedback. Besides, the broker performs the fuzzy inference system and a decision-making process for evaluating the trust level of services on the basis of processed feedback. Experimental results show that our proposed framework scores better results in terms of both trust level identification and execution efficiency. Cloud service (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Cloud broker (dpeaa)DE-He213 Decision making (dpeaa)DE-He213 Thirunavukarasu, Ramkumar aut Shanmugam, Selvamuthukumaran aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2017), 1 vom: 29. Juni, Seite 339-347 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2017 number:1 day:29 month:06 pages:339-347 https://dx.doi.org/10.1007/s40815-017-0347-5 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2017 1 29 06 339-347 |
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10.1007/s40815-017-0347-5 doi (DE-627)SPR037857770 (SPR)s40815-017-0347-5-e DE-627 ger DE-627 rakwb eng Nagarajan, Rajganesh verfasserin aut A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany 2017 Abstract With the rapid development of cloud computing services, it is highly essential to ensure the quality of service offered by the service providers. Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large number of cloud users are involved. To tackle the problem, we have advocated a big data processing framework for evaluating the trust level of availed services. An intelligent cloud broker with the incorporation of MapReduce framework has been put forth for the effective preprocessing of cloud users’ feedback. Besides, the broker performs the fuzzy inference system and a decision-making process for evaluating the trust level of services on the basis of processed feedback. Experimental results show that our proposed framework scores better results in terms of both trust level identification and execution efficiency. Cloud service (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Cloud broker (dpeaa)DE-He213 Decision making (dpeaa)DE-He213 Thirunavukarasu, Ramkumar aut Shanmugam, Selvamuthukumaran aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2017), 1 vom: 29. Juni, Seite 339-347 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2017 number:1 day:29 month:06 pages:339-347 https://dx.doi.org/10.1007/s40815-017-0347-5 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_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 20 2017 1 29 06 339-347 |
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Nagarajan, Rajganesh @@aut@@ Thirunavukarasu, Ramkumar @@aut@@ Shanmugam, Selvamuthukumaran @@aut@@ |
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Nagarajan, Rajganesh |
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Nagarajan, Rajganesh misc Cloud service misc MapReduce misc Fuzzy logic misc Cloud broker misc Decision making A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback |
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A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback Cloud service (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Cloud broker (dpeaa)DE-He213 Decision making (dpeaa)DE-He213 |
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fuzzy-based intelligent cloud broker with mapreduce framework to evaluate the trust level of cloud services using customer feedback |
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A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback |
abstract |
Abstract With the rapid development of cloud computing services, it is highly essential to ensure the quality of service offered by the service providers. Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large number of cloud users are involved. To tackle the problem, we have advocated a big data processing framework for evaluating the trust level of availed services. An intelligent cloud broker with the incorporation of MapReduce framework has been put forth for the effective preprocessing of cloud users’ feedback. Besides, the broker performs the fuzzy inference system and a decision-making process for evaluating the trust level of services on the basis of processed feedback. Experimental results show that our proposed framework scores better results in terms of both trust level identification and execution efficiency. © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany 2017 |
abstractGer |
Abstract With the rapid development of cloud computing services, it is highly essential to ensure the quality of service offered by the service providers. Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large number of cloud users are involved. To tackle the problem, we have advocated a big data processing framework for evaluating the trust level of availed services. An intelligent cloud broker with the incorporation of MapReduce framework has been put forth for the effective preprocessing of cloud users’ feedback. Besides, the broker performs the fuzzy inference system and a decision-making process for evaluating the trust level of services on the basis of processed feedback. Experimental results show that our proposed framework scores better results in terms of both trust level identification and execution efficiency. © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany 2017 |
abstract_unstemmed |
Abstract With the rapid development of cloud computing services, it is highly essential to ensure the quality of service offered by the service providers. Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large number of cloud users are involved. To tackle the problem, we have advocated a big data processing framework for evaluating the trust level of availed services. An intelligent cloud broker with the incorporation of MapReduce framework has been put forth for the effective preprocessing of cloud users’ feedback. Besides, the broker performs the fuzzy inference system and a decision-making process for evaluating the trust level of services on the basis of processed feedback. Experimental results show that our proposed framework scores better results in terms of both trust level identification and execution efficiency. © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany 2017 |
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A Fuzzy-Based Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback |
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Though several trust evaluation methods are available based on user feedback, it is hard to reap meaningful trust level of services when large number of cloud users are involved. To tackle the problem, we have advocated a big data processing framework for evaluating the trust level of availed services. An intelligent cloud broker with the incorporation of MapReduce framework has been put forth for the effective preprocessing of cloud users’ feedback. Besides, the broker performs the fuzzy inference system and a decision-making process for evaluating the trust level of services on the basis of processed feedback. 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