Optimal mobile device selection for mobile cloud service providing
Abstract With the rapid growth of the mobile devices and the emergence of cloud computing, mobile cloud computing has gained widespread interest. In mobile cloud computing, a large-scale collection of mobile devices cooperate with each other to provide a cloud service at the edge. However, the impro...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhou, Ao [verfasserIn] Wang, Shangguang [verfasserIn] Li, Jinglin [verfasserIn] Sun, Qibo [verfasserIn] Yang, Fangchun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 72(2016), 8 vom: 24. März, Seite 3222-3235 |
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| Übergeordnetes Werk: |
volume:72 ; year:2016 ; number:8 ; day:24 ; month:03 ; pages:3222-3235 |
| Links: |
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| DOI / URN: |
10.1007/s11227-016-1704-0 |
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| Katalog-ID: |
SPR017925088 |
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| 520 | |a Abstract With the rapid growth of the mobile devices and the emergence of cloud computing, mobile cloud computing has gained widespread interest. In mobile cloud computing, a large-scale collection of mobile devices cooperate with each other to provide a cloud service at the edge. However, the improper mobile device selection has a negative effect on the quality of service. Existing methods are difficult to solve the problem, because they do not take the status and the historical characteristics of the mobile devices into consideration. This paper introduces a device status-aware and stability-aware mobile device selection method. Firstly, a model is designed to store the status and the historical characteristics of each mobile device. Secondly, an optimized cloud model is employed to evaluate the stability of each mobile device. Lastly, an optimal mobile device searching algorithm is presented to select the optimal mobile device. We provide an extensive evaluation of our method. The results show that our method can increase the quality of mobile cloud service compared with the traditional method. | ||
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| 650 | 4 | |a Mobile device selection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cloud model |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Wang, Shangguang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jinglin |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Qibo |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Fangchun |e verfasserin |4 aut | |
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10.1007/s11227-016-1704-0 doi (DE-627)SPR017925088 (SPR)s11227-016-1704-0-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Zhou, Ao verfasserin aut Optimal mobile device selection for mobile cloud service providing 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract With the rapid growth of the mobile devices and the emergence of cloud computing, mobile cloud computing has gained widespread interest. In mobile cloud computing, a large-scale collection of mobile devices cooperate with each other to provide a cloud service at the edge. However, the improper mobile device selection has a negative effect on the quality of service. Existing methods are difficult to solve the problem, because they do not take the status and the historical characteristics of the mobile devices into consideration. This paper introduces a device status-aware and stability-aware mobile device selection method. Firstly, a model is designed to store the status and the historical characteristics of each mobile device. Secondly, an optimized cloud model is employed to evaluate the stability of each mobile device. Lastly, an optimal mobile device searching algorithm is presented to select the optimal mobile device. We provide an extensive evaluation of our method. The results show that our method can increase the quality of mobile cloud service compared with the traditional method. Mobile device cloud (dpeaa)DE-He213 Mobile cloud computing (dpeaa)DE-He213 Mobile cloud service (dpeaa)DE-He213 Mobile device selection (dpeaa)DE-He213 Cloud model (dpeaa)DE-He213 Wang, Shangguang verfasserin aut Li, Jinglin verfasserin aut Sun, Qibo verfasserin aut Yang, Fangchun verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 72(2016), 8 vom: 24. März, Seite 3222-3235 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:72 year:2016 number:8 day:24 month:03 pages:3222-3235 https://dx.doi.org/10.1007/s11227-016-1704-0 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_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_206 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_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 54.20 ASE AR 72 2016 8 24 03 3222-3235 |
| spelling |
10.1007/s11227-016-1704-0 doi (DE-627)SPR017925088 (SPR)s11227-016-1704-0-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Zhou, Ao verfasserin aut Optimal mobile device selection for mobile cloud service providing 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract With the rapid growth of the mobile devices and the emergence of cloud computing, mobile cloud computing has gained widespread interest. In mobile cloud computing, a large-scale collection of mobile devices cooperate with each other to provide a cloud service at the edge. However, the improper mobile device selection has a negative effect on the quality of service. Existing methods are difficult to solve the problem, because they do not take the status and the historical characteristics of the mobile devices into consideration. This paper introduces a device status-aware and stability-aware mobile device selection method. Firstly, a model is designed to store the status and the historical characteristics of each mobile device. Secondly, an optimized cloud model is employed to evaluate the stability of each mobile device. Lastly, an optimal mobile device searching algorithm is presented to select the optimal mobile device. We provide an extensive evaluation of our method. The results show that our method can increase the quality of mobile cloud service compared with the traditional method. Mobile device cloud (dpeaa)DE-He213 Mobile cloud computing (dpeaa)DE-He213 Mobile cloud service (dpeaa)DE-He213 Mobile device selection (dpeaa)DE-He213 Cloud model (dpeaa)DE-He213 Wang, Shangguang verfasserin aut Li, Jinglin verfasserin aut Sun, Qibo verfasserin aut Yang, Fangchun verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 72(2016), 8 vom: 24. März, Seite 3222-3235 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:72 year:2016 number:8 day:24 month:03 pages:3222-3235 https://dx.doi.org/10.1007/s11227-016-1704-0 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_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_206 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_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 54.20 ASE AR 72 2016 8 24 03 3222-3235 |
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10.1007/s11227-016-1704-0 doi (DE-627)SPR017925088 (SPR)s11227-016-1704-0-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Zhou, Ao verfasserin aut Optimal mobile device selection for mobile cloud service providing 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract With the rapid growth of the mobile devices and the emergence of cloud computing, mobile cloud computing has gained widespread interest. In mobile cloud computing, a large-scale collection of mobile devices cooperate with each other to provide a cloud service at the edge. However, the improper mobile device selection has a negative effect on the quality of service. Existing methods are difficult to solve the problem, because they do not take the status and the historical characteristics of the mobile devices into consideration. This paper introduces a device status-aware and stability-aware mobile device selection method. Firstly, a model is designed to store the status and the historical characteristics of each mobile device. Secondly, an optimized cloud model is employed to evaluate the stability of each mobile device. Lastly, an optimal mobile device searching algorithm is presented to select the optimal mobile device. We provide an extensive evaluation of our method. The results show that our method can increase the quality of mobile cloud service compared with the traditional method. Mobile device cloud (dpeaa)DE-He213 Mobile cloud computing (dpeaa)DE-He213 Mobile cloud service (dpeaa)DE-He213 Mobile device selection (dpeaa)DE-He213 Cloud model (dpeaa)DE-He213 Wang, Shangguang verfasserin aut Li, Jinglin verfasserin aut Sun, Qibo verfasserin aut Yang, Fangchun verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 72(2016), 8 vom: 24. März, Seite 3222-3235 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:72 year:2016 number:8 day:24 month:03 pages:3222-3235 https://dx.doi.org/10.1007/s11227-016-1704-0 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_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_206 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_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 54.20 ASE AR 72 2016 8 24 03 3222-3235 |
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10.1007/s11227-016-1704-0 doi (DE-627)SPR017925088 (SPR)s11227-016-1704-0-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Zhou, Ao verfasserin aut Optimal mobile device selection for mobile cloud service providing 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract With the rapid growth of the mobile devices and the emergence of cloud computing, mobile cloud computing has gained widespread interest. In mobile cloud computing, a large-scale collection of mobile devices cooperate with each other to provide a cloud service at the edge. However, the improper mobile device selection has a negative effect on the quality of service. Existing methods are difficult to solve the problem, because they do not take the status and the historical characteristics of the mobile devices into consideration. This paper introduces a device status-aware and stability-aware mobile device selection method. Firstly, a model is designed to store the status and the historical characteristics of each mobile device. Secondly, an optimized cloud model is employed to evaluate the stability of each mobile device. Lastly, an optimal mobile device searching algorithm is presented to select the optimal mobile device. We provide an extensive evaluation of our method. The results show that our method can increase the quality of mobile cloud service compared with the traditional method. Mobile device cloud (dpeaa)DE-He213 Mobile cloud computing (dpeaa)DE-He213 Mobile cloud service (dpeaa)DE-He213 Mobile device selection (dpeaa)DE-He213 Cloud model (dpeaa)DE-He213 Wang, Shangguang verfasserin aut Li, Jinglin verfasserin aut Sun, Qibo verfasserin aut Yang, Fangchun verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 72(2016), 8 vom: 24. März, Seite 3222-3235 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:72 year:2016 number:8 day:24 month:03 pages:3222-3235 https://dx.doi.org/10.1007/s11227-016-1704-0 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_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_206 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_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 54.20 ASE AR 72 2016 8 24 03 3222-3235 |
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10.1007/s11227-016-1704-0 doi (DE-627)SPR017925088 (SPR)s11227-016-1704-0-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Zhou, Ao verfasserin aut Optimal mobile device selection for mobile cloud service providing 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract With the rapid growth of the mobile devices and the emergence of cloud computing, mobile cloud computing has gained widespread interest. In mobile cloud computing, a large-scale collection of mobile devices cooperate with each other to provide a cloud service at the edge. However, the improper mobile device selection has a negative effect on the quality of service. Existing methods are difficult to solve the problem, because they do not take the status and the historical characteristics of the mobile devices into consideration. This paper introduces a device status-aware and stability-aware mobile device selection method. Firstly, a model is designed to store the status and the historical characteristics of each mobile device. Secondly, an optimized cloud model is employed to evaluate the stability of each mobile device. Lastly, an optimal mobile device searching algorithm is presented to select the optimal mobile device. We provide an extensive evaluation of our method. The results show that our method can increase the quality of mobile cloud service compared with the traditional method. Mobile device cloud (dpeaa)DE-He213 Mobile cloud computing (dpeaa)DE-He213 Mobile cloud service (dpeaa)DE-He213 Mobile device selection (dpeaa)DE-He213 Cloud model (dpeaa)DE-He213 Wang, Shangguang verfasserin aut Li, Jinglin verfasserin aut Sun, Qibo verfasserin aut Yang, Fangchun verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 72(2016), 8 vom: 24. März, Seite 3222-3235 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:72 year:2016 number:8 day:24 month:03 pages:3222-3235 https://dx.doi.org/10.1007/s11227-016-1704-0 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_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_206 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_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 54.20 ASE AR 72 2016 8 24 03 3222-3235 |
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Zhou, Ao @@aut@@ Wang, Shangguang @@aut@@ Li, Jinglin @@aut@@ Sun, Qibo @@aut@@ Yang, Fangchun @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR017925088</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111054338.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11227-016-1704-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR017925088</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11227-016-1704-0-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.20</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhou, Ao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Optimal mobile device selection for mobile cloud service providing</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract With the rapid growth of the mobile devices and the emergence of cloud computing, mobile cloud computing has gained widespread interest. In mobile cloud computing, a large-scale collection of mobile devices cooperate with each other to provide a cloud service at the edge. However, the improper mobile device selection has a negative effect on the quality of service. Existing methods are difficult to solve the problem, because they do not take the status and the historical characteristics of the mobile devices into consideration. This paper introduces a device status-aware and stability-aware mobile device selection method. Firstly, a model is designed to store the status and the historical characteristics of each mobile device. Secondly, an optimized cloud model is employed to evaluate the stability of each mobile device. Lastly, an optimal mobile device searching algorithm is presented to select the optimal mobile device. We provide an extensive evaluation of our method. The results show that our method can increase the quality of mobile cloud service compared with the traditional method.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mobile device cloud</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mobile cloud computing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mobile cloud service</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mobile device selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cloud model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Shangguang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jinglin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Qibo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Fangchun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The journal of supercomputing</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987</subfield><subfield code="g">72(2016), 8 vom: 24. März, Seite 3222-3235</subfield><subfield code="w">(DE-627)271350202</subfield><subfield code="w">(DE-600)1479917-0</subfield><subfield code="x">1573-0484</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:72</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:8</subfield><subfield code="g">day:24</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:3222-3235</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11227-016-1704-0</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield 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Zhou, Ao |
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Zhou, Ao ddc 004 bkl 54.20 misc Mobile device cloud misc Mobile cloud computing misc Mobile cloud service misc Mobile device selection misc Cloud model Optimal mobile device selection for mobile cloud service providing |
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Zhou, Ao Wang, Shangguang Li, Jinglin Sun, Qibo Yang, Fangchun |
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optimal mobile device selection for mobile cloud service providing |
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Optimal mobile device selection for mobile cloud service providing |
| abstract |
Abstract With the rapid growth of the mobile devices and the emergence of cloud computing, mobile cloud computing has gained widespread interest. In mobile cloud computing, a large-scale collection of mobile devices cooperate with each other to provide a cloud service at the edge. However, the improper mobile device selection has a negative effect on the quality of service. Existing methods are difficult to solve the problem, because they do not take the status and the historical characteristics of the mobile devices into consideration. This paper introduces a device status-aware and stability-aware mobile device selection method. Firstly, a model is designed to store the status and the historical characteristics of each mobile device. Secondly, an optimized cloud model is employed to evaluate the stability of each mobile device. Lastly, an optimal mobile device searching algorithm is presented to select the optimal mobile device. We provide an extensive evaluation of our method. The results show that our method can increase the quality of mobile cloud service compared with the traditional method. |
| abstractGer |
Abstract With the rapid growth of the mobile devices and the emergence of cloud computing, mobile cloud computing has gained widespread interest. In mobile cloud computing, a large-scale collection of mobile devices cooperate with each other to provide a cloud service at the edge. However, the improper mobile device selection has a negative effect on the quality of service. Existing methods are difficult to solve the problem, because they do not take the status and the historical characteristics of the mobile devices into consideration. This paper introduces a device status-aware and stability-aware mobile device selection method. Firstly, a model is designed to store the status and the historical characteristics of each mobile device. Secondly, an optimized cloud model is employed to evaluate the stability of each mobile device. Lastly, an optimal mobile device searching algorithm is presented to select the optimal mobile device. We provide an extensive evaluation of our method. The results show that our method can increase the quality of mobile cloud service compared with the traditional method. |
| abstract_unstemmed |
Abstract With the rapid growth of the mobile devices and the emergence of cloud computing, mobile cloud computing has gained widespread interest. In mobile cloud computing, a large-scale collection of mobile devices cooperate with each other to provide a cloud service at the edge. However, the improper mobile device selection has a negative effect on the quality of service. Existing methods are difficult to solve the problem, because they do not take the status and the historical characteristics of the mobile devices into consideration. This paper introduces a device status-aware and stability-aware mobile device selection method. Firstly, a model is designed to store the status and the historical characteristics of each mobile device. Secondly, an optimized cloud model is employed to evaluate the stability of each mobile device. Lastly, an optimal mobile device searching algorithm is presented to select the optimal mobile device. We provide an extensive evaluation of our method. The results show that our method can increase the quality of mobile cloud service compared with the traditional method. |
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| title_short |
Optimal mobile device selection for mobile cloud service providing |
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https://dx.doi.org/10.1007/s11227-016-1704-0 |
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true |
| author2 |
Wang, Shangguang Li, Jinglin Sun, Qibo Yang, Fangchun |
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Wang, Shangguang Li, Jinglin Sun, Qibo Yang, Fangchun |
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| doi_str |
10.1007/s11227-016-1704-0 |
| up_date |
2024-07-03T16:07:27.580Z |
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| score |
7.399686 |