Power-Aware Resource Reconfiguration Using Genetic Algorithm in Cloud Computing
Cloud computing enables scalable computation based on virtualization technology. However, current resource reallocation solution seldom considers the stability of virtual machine (VM) placement pattern. Varied workloads of applications would lead to frequent resource reconfiguration requirements due...
Ausführliche Beschreibung
Autor*in: |
Li Deng [verfasserIn] Yang Li [verfasserIn] Li Yao [verfasserIn] Yu Jin [verfasserIn] Jinguang Gu [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Übergeordnetes Werk: |
In: Mobile Information Systems - Hindawi Limited, 2015, (2016) |
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Übergeordnetes Werk: |
year:2016 |
Links: |
Link aufrufen |
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DOI / URN: |
10.1155/2016/4859862 |
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Katalog-ID: |
DOAJ058262733 |
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10.1155/2016/4859862 doi (DE-627)DOAJ058262733 (DE-599)DOAJcf710358c8f445bba566bc4082320ab5 DE-627 ger DE-627 rakwb eng TK5101-6720 Li Deng verfasserin aut Power-Aware Resource Reconfiguration Using Genetic Algorithm in Cloud Computing 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cloud computing enables scalable computation based on virtualization technology. However, current resource reallocation solution seldom considers the stability of virtual machine (VM) placement pattern. Varied workloads of applications would lead to frequent resource reconfiguration requirements due to repeated appearance of hot nodes. In this paper, several algorithms for VM placement (multiobjective genetic algorithm (MOGA), power-aware multiobjective genetic algorithm (pMOGA), and enhanced power-aware multiobjective genetic algorithm (EpMOGA)) are presented to improve stability of VM placement pattern with less migration overhead. The energy consumption is also considered. A type-matching controller is designed to improve evolution process. Nondominated sorting genetic algorithm II (NSGAII) is used to select new generations during evolution process. Our simulation results demonstrate that these algorithms all provide resource reallocation solutions with long stabilization time of nodes. pMOGA and EpMOGA also better balance the relationship of stabilization and energy efficiency by adding number of active nodes as one of optimal objectives. Type-matching controller makes EpMOGA superior to pMOGA. Telecommunication Yang Li verfasserin aut Li Yao verfasserin aut Yu Jin verfasserin aut Jinguang Gu verfasserin aut In Mobile Information Systems Hindawi Limited, 2015 (2016) (DE-627)486725774 (DE-600)2187808-0 1875905X nnns year:2016 https://doi.org/10.1155/2016/4859862 kostenfrei https://doaj.org/article/cf710358c8f445bba566bc4082320ab5 kostenfrei http://dx.doi.org/10.1155/2016/4859862 kostenfrei https://doaj.org/toc/1574-017X Journal toc kostenfrei https://doaj.org/toc/1875-905X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2016 |
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10.1155/2016/4859862 doi (DE-627)DOAJ058262733 (DE-599)DOAJcf710358c8f445bba566bc4082320ab5 DE-627 ger DE-627 rakwb eng TK5101-6720 Li Deng verfasserin aut Power-Aware Resource Reconfiguration Using Genetic Algorithm in Cloud Computing 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cloud computing enables scalable computation based on virtualization technology. However, current resource reallocation solution seldom considers the stability of virtual machine (VM) placement pattern. Varied workloads of applications would lead to frequent resource reconfiguration requirements due to repeated appearance of hot nodes. In this paper, several algorithms for VM placement (multiobjective genetic algorithm (MOGA), power-aware multiobjective genetic algorithm (pMOGA), and enhanced power-aware multiobjective genetic algorithm (EpMOGA)) are presented to improve stability of VM placement pattern with less migration overhead. The energy consumption is also considered. A type-matching controller is designed to improve evolution process. Nondominated sorting genetic algorithm II (NSGAII) is used to select new generations during evolution process. Our simulation results demonstrate that these algorithms all provide resource reallocation solutions with long stabilization time of nodes. pMOGA and EpMOGA also better balance the relationship of stabilization and energy efficiency by adding number of active nodes as one of optimal objectives. Type-matching controller makes EpMOGA superior to pMOGA. Telecommunication Yang Li verfasserin aut Li Yao verfasserin aut Yu Jin verfasserin aut Jinguang Gu verfasserin aut In Mobile Information Systems Hindawi Limited, 2015 (2016) (DE-627)486725774 (DE-600)2187808-0 1875905X nnns year:2016 https://doi.org/10.1155/2016/4859862 kostenfrei https://doaj.org/article/cf710358c8f445bba566bc4082320ab5 kostenfrei http://dx.doi.org/10.1155/2016/4859862 kostenfrei https://doaj.org/toc/1574-017X Journal toc kostenfrei https://doaj.org/toc/1875-905X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2016 |
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10.1155/2016/4859862 doi (DE-627)DOAJ058262733 (DE-599)DOAJcf710358c8f445bba566bc4082320ab5 DE-627 ger DE-627 rakwb eng TK5101-6720 Li Deng verfasserin aut Power-Aware Resource Reconfiguration Using Genetic Algorithm in Cloud Computing 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cloud computing enables scalable computation based on virtualization technology. However, current resource reallocation solution seldom considers the stability of virtual machine (VM) placement pattern. Varied workloads of applications would lead to frequent resource reconfiguration requirements due to repeated appearance of hot nodes. In this paper, several algorithms for VM placement (multiobjective genetic algorithm (MOGA), power-aware multiobjective genetic algorithm (pMOGA), and enhanced power-aware multiobjective genetic algorithm (EpMOGA)) are presented to improve stability of VM placement pattern with less migration overhead. The energy consumption is also considered. A type-matching controller is designed to improve evolution process. Nondominated sorting genetic algorithm II (NSGAII) is used to select new generations during evolution process. Our simulation results demonstrate that these algorithms all provide resource reallocation solutions with long stabilization time of nodes. pMOGA and EpMOGA also better balance the relationship of stabilization and energy efficiency by adding number of active nodes as one of optimal objectives. Type-matching controller makes EpMOGA superior to pMOGA. Telecommunication Yang Li verfasserin aut Li Yao verfasserin aut Yu Jin verfasserin aut Jinguang Gu verfasserin aut In Mobile Information Systems Hindawi Limited, 2015 (2016) (DE-627)486725774 (DE-600)2187808-0 1875905X nnns year:2016 https://doi.org/10.1155/2016/4859862 kostenfrei https://doaj.org/article/cf710358c8f445bba566bc4082320ab5 kostenfrei http://dx.doi.org/10.1155/2016/4859862 kostenfrei https://doaj.org/toc/1574-017X Journal toc kostenfrei https://doaj.org/toc/1875-905X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2016 |
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10.1155/2016/4859862 doi (DE-627)DOAJ058262733 (DE-599)DOAJcf710358c8f445bba566bc4082320ab5 DE-627 ger DE-627 rakwb eng TK5101-6720 Li Deng verfasserin aut Power-Aware Resource Reconfiguration Using Genetic Algorithm in Cloud Computing 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cloud computing enables scalable computation based on virtualization technology. However, current resource reallocation solution seldom considers the stability of virtual machine (VM) placement pattern. Varied workloads of applications would lead to frequent resource reconfiguration requirements due to repeated appearance of hot nodes. In this paper, several algorithms for VM placement (multiobjective genetic algorithm (MOGA), power-aware multiobjective genetic algorithm (pMOGA), and enhanced power-aware multiobjective genetic algorithm (EpMOGA)) are presented to improve stability of VM placement pattern with less migration overhead. The energy consumption is also considered. A type-matching controller is designed to improve evolution process. Nondominated sorting genetic algorithm II (NSGAII) is used to select new generations during evolution process. Our simulation results demonstrate that these algorithms all provide resource reallocation solutions with long stabilization time of nodes. pMOGA and EpMOGA also better balance the relationship of stabilization and energy efficiency by adding number of active nodes as one of optimal objectives. Type-matching controller makes EpMOGA superior to pMOGA. Telecommunication Yang Li verfasserin aut Li Yao verfasserin aut Yu Jin verfasserin aut Jinguang Gu verfasserin aut In Mobile Information Systems Hindawi Limited, 2015 (2016) (DE-627)486725774 (DE-600)2187808-0 1875905X nnns year:2016 https://doi.org/10.1155/2016/4859862 kostenfrei https://doaj.org/article/cf710358c8f445bba566bc4082320ab5 kostenfrei http://dx.doi.org/10.1155/2016/4859862 kostenfrei https://doaj.org/toc/1574-017X Journal toc kostenfrei https://doaj.org/toc/1875-905X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2016 |
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Power-Aware Resource Reconfiguration Using Genetic Algorithm in Cloud Computing |
abstract |
Cloud computing enables scalable computation based on virtualization technology. However, current resource reallocation solution seldom considers the stability of virtual machine (VM) placement pattern. Varied workloads of applications would lead to frequent resource reconfiguration requirements due to repeated appearance of hot nodes. In this paper, several algorithms for VM placement (multiobjective genetic algorithm (MOGA), power-aware multiobjective genetic algorithm (pMOGA), and enhanced power-aware multiobjective genetic algorithm (EpMOGA)) are presented to improve stability of VM placement pattern with less migration overhead. The energy consumption is also considered. A type-matching controller is designed to improve evolution process. Nondominated sorting genetic algorithm II (NSGAII) is used to select new generations during evolution process. Our simulation results demonstrate that these algorithms all provide resource reallocation solutions with long stabilization time of nodes. pMOGA and EpMOGA also better balance the relationship of stabilization and energy efficiency by adding number of active nodes as one of optimal objectives. Type-matching controller makes EpMOGA superior to pMOGA. |
abstractGer |
Cloud computing enables scalable computation based on virtualization technology. However, current resource reallocation solution seldom considers the stability of virtual machine (VM) placement pattern. Varied workloads of applications would lead to frequent resource reconfiguration requirements due to repeated appearance of hot nodes. In this paper, several algorithms for VM placement (multiobjective genetic algorithm (MOGA), power-aware multiobjective genetic algorithm (pMOGA), and enhanced power-aware multiobjective genetic algorithm (EpMOGA)) are presented to improve stability of VM placement pattern with less migration overhead. The energy consumption is also considered. A type-matching controller is designed to improve evolution process. Nondominated sorting genetic algorithm II (NSGAII) is used to select new generations during evolution process. Our simulation results demonstrate that these algorithms all provide resource reallocation solutions with long stabilization time of nodes. pMOGA and EpMOGA also better balance the relationship of stabilization and energy efficiency by adding number of active nodes as one of optimal objectives. Type-matching controller makes EpMOGA superior to pMOGA. |
abstract_unstemmed |
Cloud computing enables scalable computation based on virtualization technology. However, current resource reallocation solution seldom considers the stability of virtual machine (VM) placement pattern. Varied workloads of applications would lead to frequent resource reconfiguration requirements due to repeated appearance of hot nodes. In this paper, several algorithms for VM placement (multiobjective genetic algorithm (MOGA), power-aware multiobjective genetic algorithm (pMOGA), and enhanced power-aware multiobjective genetic algorithm (EpMOGA)) are presented to improve stability of VM placement pattern with less migration overhead. The energy consumption is also considered. A type-matching controller is designed to improve evolution process. Nondominated sorting genetic algorithm II (NSGAII) is used to select new generations during evolution process. Our simulation results demonstrate that these algorithms all provide resource reallocation solutions with long stabilization time of nodes. pMOGA and EpMOGA also better balance the relationship of stabilization and energy efficiency by adding number of active nodes as one of optimal objectives. Type-matching controller makes EpMOGA superior to pMOGA. |
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