A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment

Abstract Cloud computing is one of the rapidly growing environment in recent days where it interconnects the entire world in human’s day to day life activities. Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing....
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Muthulakshmi, B. [verfasserIn] Somasundaram, K. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Cloud computing Scheduling Load balancing Virtualization Client–server communication

Übergeordnetes Werk:	Enthalten in: Cluster computing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998, 22(2017), Suppl 5 vom: 19. Sept., Seite 10769-10777
Übergeordnetes Werk:	volume:22 ; year:2017 ; number:Suppl 5 ; day:19 ; month:09 ; pages:10769-10777

Links:	Volltext

DOI / URN:	10.1007/s10586-017-1174-z

Katalog-ID:	SPR011521260

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520			\|a Abstract Cloud computing is one of the rapidly growing environment in recent days where it interconnects the entire world in human’s day to day life activities. Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing. To do this and choose an optimum resource, optimum schedule and through this balancing the load can be obtained using ABC-SA method. The main contribution of this paper is to implement a hybrid optimization algorithm by integrating the functionality of simulated annealing (SA) into artificial bee colony (ABC) algorithm to do the efficient scheduling according to the task size, priority of the request and closest distance between client nodes to a server in the cloud environment. This ABC-SA based optimized scheduling approach has the capability of improving the efficiency in terms of searching optimum resource time where the dynamic and random searching behavior is obtained from SA. ABC-SA is implemented and experimented in the CloudSim tool and the results are verified. The performance of the proposed approach is evaluated by comparing the results with the existing system results.
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700	1		\|a Somasundaram, K. \|e verfasserin \|4 aut
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912			\|a GBV_ILN_213
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912			\|a GBV_ILN_281
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912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
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bklnumber	54.50 54.32 54.25
publishDate	2017
allfields	10.1007/s10586-017-1174-z doi (DE-627)SPR011521260 (SPR)s10586-017-1174-z-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Muthulakshmi, B. verfasserin aut A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cloud computing is one of the rapidly growing environment in recent days where it interconnects the entire world in human’s day to day life activities. Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing. To do this and choose an optimum resource, optimum schedule and through this balancing the load can be obtained using ABC-SA method. The main contribution of this paper is to implement a hybrid optimization algorithm by integrating the functionality of simulated annealing (SA) into artificial bee colony (ABC) algorithm to do the efficient scheduling according to the task size, priority of the request and closest distance between client nodes to a server in the cloud environment. This ABC-SA based optimized scheduling approach has the capability of improving the efficiency in terms of searching optimum resource time where the dynamic and random searching behavior is obtained from SA. ABC-SA is implemented and experimented in the CloudSim tool and the results are verified. The performance of the proposed approach is evaluated by comparing the results with the existing system results. Cloud computing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Load balancing (dpeaa)DE-He213 Virtualization (dpeaa)DE-He213 Client–server communication (dpeaa)DE-He213 Somasundaram, K. verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2017), Suppl 5 vom: 19. Sept., Seite 10769-10777 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2017 number:Suppl 5 day:19 month:09 pages:10769-10777 https://dx.doi.org/10.1007/s10586-017-1174-z 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_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 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2017 Suppl 5 19 09 10769-10777
spelling	10.1007/s10586-017-1174-z doi (DE-627)SPR011521260 (SPR)s10586-017-1174-z-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Muthulakshmi, B. verfasserin aut A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cloud computing is one of the rapidly growing environment in recent days where it interconnects the entire world in human’s day to day life activities. Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing. To do this and choose an optimum resource, optimum schedule and through this balancing the load can be obtained using ABC-SA method. The main contribution of this paper is to implement a hybrid optimization algorithm by integrating the functionality of simulated annealing (SA) into artificial bee colony (ABC) algorithm to do the efficient scheduling according to the task size, priority of the request and closest distance between client nodes to a server in the cloud environment. This ABC-SA based optimized scheduling approach has the capability of improving the efficiency in terms of searching optimum resource time where the dynamic and random searching behavior is obtained from SA. ABC-SA is implemented and experimented in the CloudSim tool and the results are verified. The performance of the proposed approach is evaluated by comparing the results with the existing system results. Cloud computing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Load balancing (dpeaa)DE-He213 Virtualization (dpeaa)DE-He213 Client–server communication (dpeaa)DE-He213 Somasundaram, K. verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2017), Suppl 5 vom: 19. Sept., Seite 10769-10777 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2017 number:Suppl 5 day:19 month:09 pages:10769-10777 https://dx.doi.org/10.1007/s10586-017-1174-z 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_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 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2017 Suppl 5 19 09 10769-10777
allfields_unstemmed	10.1007/s10586-017-1174-z doi (DE-627)SPR011521260 (SPR)s10586-017-1174-z-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Muthulakshmi, B. verfasserin aut A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cloud computing is one of the rapidly growing environment in recent days where it interconnects the entire world in human’s day to day life activities. Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing. To do this and choose an optimum resource, optimum schedule and through this balancing the load can be obtained using ABC-SA method. The main contribution of this paper is to implement a hybrid optimization algorithm by integrating the functionality of simulated annealing (SA) into artificial bee colony (ABC) algorithm to do the efficient scheduling according to the task size, priority of the request and closest distance between client nodes to a server in the cloud environment. This ABC-SA based optimized scheduling approach has the capability of improving the efficiency in terms of searching optimum resource time where the dynamic and random searching behavior is obtained from SA. ABC-SA is implemented and experimented in the CloudSim tool and the results are verified. The performance of the proposed approach is evaluated by comparing the results with the existing system results. Cloud computing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Load balancing (dpeaa)DE-He213 Virtualization (dpeaa)DE-He213 Client–server communication (dpeaa)DE-He213 Somasundaram, K. verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2017), Suppl 5 vom: 19. Sept., Seite 10769-10777 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2017 number:Suppl 5 day:19 month:09 pages:10769-10777 https://dx.doi.org/10.1007/s10586-017-1174-z 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_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 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2017 Suppl 5 19 09 10769-10777
allfieldsGer	10.1007/s10586-017-1174-z doi (DE-627)SPR011521260 (SPR)s10586-017-1174-z-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Muthulakshmi, B. verfasserin aut A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cloud computing is one of the rapidly growing environment in recent days where it interconnects the entire world in human’s day to day life activities. Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing. To do this and choose an optimum resource, optimum schedule and through this balancing the load can be obtained using ABC-SA method. The main contribution of this paper is to implement a hybrid optimization algorithm by integrating the functionality of simulated annealing (SA) into artificial bee colony (ABC) algorithm to do the efficient scheduling according to the task size, priority of the request and closest distance between client nodes to a server in the cloud environment. This ABC-SA based optimized scheduling approach has the capability of improving the efficiency in terms of searching optimum resource time where the dynamic and random searching behavior is obtained from SA. ABC-SA is implemented and experimented in the CloudSim tool and the results are verified. The performance of the proposed approach is evaluated by comparing the results with the existing system results. Cloud computing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Load balancing (dpeaa)DE-He213 Virtualization (dpeaa)DE-He213 Client–server communication (dpeaa)DE-He213 Somasundaram, K. verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2017), Suppl 5 vom: 19. Sept., Seite 10769-10777 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2017 number:Suppl 5 day:19 month:09 pages:10769-10777 https://dx.doi.org/10.1007/s10586-017-1174-z 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_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 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2017 Suppl 5 19 09 10769-10777
allfieldsSound	10.1007/s10586-017-1174-z doi (DE-627)SPR011521260 (SPR)s10586-017-1174-z-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Muthulakshmi, B. verfasserin aut A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cloud computing is one of the rapidly growing environment in recent days where it interconnects the entire world in human’s day to day life activities. Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing. To do this and choose an optimum resource, optimum schedule and through this balancing the load can be obtained using ABC-SA method. The main contribution of this paper is to implement a hybrid optimization algorithm by integrating the functionality of simulated annealing (SA) into artificial bee colony (ABC) algorithm to do the efficient scheduling according to the task size, priority of the request and closest distance between client nodes to a server in the cloud environment. This ABC-SA based optimized scheduling approach has the capability of improving the efficiency in terms of searching optimum resource time where the dynamic and random searching behavior is obtained from SA. ABC-SA is implemented and experimented in the CloudSim tool and the results are verified. The performance of the proposed approach is evaluated by comparing the results with the existing system results. Cloud computing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Load balancing (dpeaa)DE-He213 Virtualization (dpeaa)DE-He213 Client–server communication (dpeaa)DE-He213 Somasundaram, K. verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2017), Suppl 5 vom: 19. Sept., Seite 10769-10777 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2017 number:Suppl 5 day:19 month:09 pages:10769-10777 https://dx.doi.org/10.1007/s10586-017-1174-z 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_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 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2017 Suppl 5 19 09 10769-10777
language	English
source	Enthalten in Cluster computing 22(2017), Suppl 5 vom: 19. Sept., Seite 10769-10777 volume:22 year:2017 number:Suppl 5 day:19 month:09 pages:10769-10777
sourceStr	Enthalten in Cluster computing 22(2017), Suppl 5 vom: 19. Sept., Seite 10769-10777 volume:22 year:2017 number:Suppl 5 day:19 month:09 pages:10769-10777
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cloud computing Scheduling Load balancing Virtualization Client–server communication
dewey-raw	004
isfreeaccess_bool	false
container_title	Cluster computing
authorswithroles_txt_mv	Muthulakshmi, B. @@aut@@ Somasundaram, K. @@aut@@
publishDateDaySort_date	2017-09-19T00:00:00Z
hierarchy_top_id	320505332
dewey-sort	14
id	SPR011521260
language_de	englisch
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Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing. To do this and choose an optimum resource, optimum schedule and through this balancing the load can be obtained using ABC-SA method. The main contribution of this paper is to implement a hybrid optimization algorithm by integrating the functionality of simulated annealing (SA) into artificial bee colony (ABC) algorithm to do the efficient scheduling according to the task size, priority of the request and closest distance between client nodes to a server in the cloud environment. This ABC-SA based optimized scheduling approach has the capability of improving the efficiency in terms of searching optimum resource time where the dynamic and random searching behavior is obtained from SA. ABC-SA is implemented and experimented in the CloudSim tool and the results are verified. 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author	Muthulakshmi, B.
spellingShingle	Muthulakshmi, B. ddc 004 bkl 54.50 bkl 54.32 bkl 54.25 misc Cloud computing misc Scheduling misc Load balancing misc Virtualization misc Client–server communication A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment
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topic_title	004 ASE 54.50 bkl 54.32 bkl 54.25 bkl A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment Cloud computing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Load balancing (dpeaa)DE-He213 Virtualization (dpeaa)DE-He213 Client–server communication (dpeaa)DE-He213
topic	ddc 004 bkl 54.50 bkl 54.32 bkl 54.25 misc Cloud computing misc Scheduling misc Load balancing misc Virtualization misc Client–server communication
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title	A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment
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title_full	A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment
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title_sort	hybrid abc-sa based optimized scheduling and resource allocation for cloud environment
title_auth	A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment
abstract	Abstract Cloud computing is one of the rapidly growing environment in recent days where it interconnects the entire world in human’s day to day life activities. Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing. To do this and choose an optimum resource, optimum schedule and through this balancing the load can be obtained using ABC-SA method. The main contribution of this paper is to implement a hybrid optimization algorithm by integrating the functionality of simulated annealing (SA) into artificial bee colony (ABC) algorithm to do the efficient scheduling according to the task size, priority of the request and closest distance between client nodes to a server in the cloud environment. This ABC-SA based optimized scheduling approach has the capability of improving the efficiency in terms of searching optimum resource time where the dynamic and random searching behavior is obtained from SA. ABC-SA is implemented and experimented in the CloudSim tool and the results are verified. The performance of the proposed approach is evaluated by comparing the results with the existing system results.
abstractGer	Abstract Cloud computing is one of the rapidly growing environment in recent days where it interconnects the entire world in human’s day to day life activities. Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing. To do this and choose an optimum resource, optimum schedule and through this balancing the load can be obtained using ABC-SA method. The main contribution of this paper is to implement a hybrid optimization algorithm by integrating the functionality of simulated annealing (SA) into artificial bee colony (ABC) algorithm to do the efficient scheduling according to the task size, priority of the request and closest distance between client nodes to a server in the cloud environment. This ABC-SA based optimized scheduling approach has the capability of improving the efficiency in terms of searching optimum resource time where the dynamic and random searching behavior is obtained from SA. ABC-SA is implemented and experimented in the CloudSim tool and the results are verified. The performance of the proposed approach is evaluated by comparing the results with the existing system results.
abstract_unstemmed	Abstract Cloud computing is one of the rapidly growing environment in recent days where it interconnects the entire world in human’s day to day life activities. Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing. To do this and choose an optimum resource, optimum schedule and through this balancing the load can be obtained using ABC-SA method. The main contribution of this paper is to implement a hybrid optimization algorithm by integrating the functionality of simulated annealing (SA) into artificial bee colony (ABC) algorithm to do the efficient scheduling according to the task size, priority of the request and closest distance between client nodes to a server in the cloud environment. This ABC-SA based optimized scheduling approach has the capability of improving the efficiency in terms of searching optimum resource time where the dynamic and random searching behavior is obtained from SA. ABC-SA is implemented and experimented in the CloudSim tool and the results are verified. The performance of the proposed approach is evaluated by comparing the results with the existing system results.
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container_issue	Suppl 5
title_short	A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment
url	https://dx.doi.org/10.1007/s10586-017-1174-z
remote_bool	true
author2	Somasundaram, K.
author2Str	Somasundaram, K.
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doi_str	10.1007/s10586-017-1174-z
up_date	2024-07-03T23:09:10.895Z
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