Chemical reaction optimization (CRO) for cloud job scheduling

Abstract Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users’ submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimu...
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Autor*in:	Zain, Alneel Mohammed [verfasserIn] Yousif, Adil

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Chemical reaction optimization Cloud computing Scheduling Metaheuristics

Anmerkung:	© Springer Nature Switzerland AG 2019

Übergeordnetes Werk:	Enthalten in: SN applied sciences - [Cham] : Springer International Publishing, 2019, 2(2019), 1 vom: 09. Dez.
Übergeordnetes Werk:	volume:2 ; year:2019 ; number:1 ; day:09 ; month:12

Links:	Volltext

DOI / URN:	10.1007/s42452-019-1758-8

Katalog-ID:	SPR03858087X

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520			\|a Abstract Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users’ submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimum scheduling solution, such as genetic algorithm, glowworm swarm optimization and cat swarm optimization. This study introduced an optimal metaheuristic job scheduling method using chemical reaction optimization (CRO). Chemical reaction optimization is a new metaheuristic algorithm inspired from the interactions of molecules to achieve the lowest energy state possible during a chemical reaction. CRO mimics molecules’ interaction in chemical reaction microscopically. The CRO mechanism simulates the interactions of chemical reaction molecules to find out the lowest energy value possible. The proposed mechanism represents the molecular structure of each molecule as a vector of integer values, each of which represents a feasible cloud job scheduling solution. The potential energy (%$PE%$) of each molecule represents its fitness in the objective function that denotes cloud scheduling execution time. Simulation using CloudSim simulator is used to evaluate the proposed CRO mechanism. A comparison with glowworm swarm optimization, cat swarm optimization and first come first served scheduling mechanism is made. The results of simulation showed that the CRO scheduling method has the shortest execution time among all other scheduling mechanisms.
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allfields	10.1007/s42452-019-1758-8 doi (DE-627)SPR03858087X (SPR)s42452-019-1758-8-e DE-627 ger DE-627 rakwb eng Zain, Alneel Mohammed verfasserin aut Chemical reaction optimization (CRO) for cloud job scheduling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users’ submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimum scheduling solution, such as genetic algorithm, glowworm swarm optimization and cat swarm optimization. This study introduced an optimal metaheuristic job scheduling method using chemical reaction optimization (CRO). Chemical reaction optimization is a new metaheuristic algorithm inspired from the interactions of molecules to achieve the lowest energy state possible during a chemical reaction. CRO mimics molecules’ interaction in chemical reaction microscopically. The CRO mechanism simulates the interactions of chemical reaction molecules to find out the lowest energy value possible. The proposed mechanism represents the molecular structure of each molecule as a vector of integer values, each of which represents a feasible cloud job scheduling solution. The potential energy (%$PE%$) of each molecule represents its fitness in the objective function that denotes cloud scheduling execution time. Simulation using CloudSim simulator is used to evaluate the proposed CRO mechanism. A comparison with glowworm swarm optimization, cat swarm optimization and first come first served scheduling mechanism is made. The results of simulation showed that the CRO scheduling method has the shortest execution time among all other scheduling mechanisms. Chemical reaction optimization (dpeaa)DE-He213 Cloud computing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Metaheuristics (dpeaa)DE-He213 Yousif, Adil (orcid)0000-0002-7584-5775 aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2019), 1 vom: 09. Dez. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2019 number:1 day:09 month:12 https://dx.doi.org/10.1007/s42452-019-1758-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 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_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_2007 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_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2019 1 09 12
spelling	10.1007/s42452-019-1758-8 doi (DE-627)SPR03858087X (SPR)s42452-019-1758-8-e DE-627 ger DE-627 rakwb eng Zain, Alneel Mohammed verfasserin aut Chemical reaction optimization (CRO) for cloud job scheduling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users’ submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimum scheduling solution, such as genetic algorithm, glowworm swarm optimization and cat swarm optimization. This study introduced an optimal metaheuristic job scheduling method using chemical reaction optimization (CRO). Chemical reaction optimization is a new metaheuristic algorithm inspired from the interactions of molecules to achieve the lowest energy state possible during a chemical reaction. CRO mimics molecules’ interaction in chemical reaction microscopically. The CRO mechanism simulates the interactions of chemical reaction molecules to find out the lowest energy value possible. The proposed mechanism represents the molecular structure of each molecule as a vector of integer values, each of which represents a feasible cloud job scheduling solution. The potential energy (%$PE%$) of each molecule represents its fitness in the objective function that denotes cloud scheduling execution time. Simulation using CloudSim simulator is used to evaluate the proposed CRO mechanism. A comparison with glowworm swarm optimization, cat swarm optimization and first come first served scheduling mechanism is made. The results of simulation showed that the CRO scheduling method has the shortest execution time among all other scheduling mechanisms. Chemical reaction optimization (dpeaa)DE-He213 Cloud computing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Metaheuristics (dpeaa)DE-He213 Yousif, Adil (orcid)0000-0002-7584-5775 aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2019), 1 vom: 09. Dez. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2019 number:1 day:09 month:12 https://dx.doi.org/10.1007/s42452-019-1758-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 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_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_2007 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_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2019 1 09 12
allfields_unstemmed	10.1007/s42452-019-1758-8 doi (DE-627)SPR03858087X (SPR)s42452-019-1758-8-e DE-627 ger DE-627 rakwb eng Zain, Alneel Mohammed verfasserin aut Chemical reaction optimization (CRO) for cloud job scheduling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users’ submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimum scheduling solution, such as genetic algorithm, glowworm swarm optimization and cat swarm optimization. This study introduced an optimal metaheuristic job scheduling method using chemical reaction optimization (CRO). Chemical reaction optimization is a new metaheuristic algorithm inspired from the interactions of molecules to achieve the lowest energy state possible during a chemical reaction. CRO mimics molecules’ interaction in chemical reaction microscopically. The CRO mechanism simulates the interactions of chemical reaction molecules to find out the lowest energy value possible. The proposed mechanism represents the molecular structure of each molecule as a vector of integer values, each of which represents a feasible cloud job scheduling solution. The potential energy (%$PE%$) of each molecule represents its fitness in the objective function that denotes cloud scheduling execution time. Simulation using CloudSim simulator is used to evaluate the proposed CRO mechanism. A comparison with glowworm swarm optimization, cat swarm optimization and first come first served scheduling mechanism is made. The results of simulation showed that the CRO scheduling method has the shortest execution time among all other scheduling mechanisms. Chemical reaction optimization (dpeaa)DE-He213 Cloud computing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Metaheuristics (dpeaa)DE-He213 Yousif, Adil (orcid)0000-0002-7584-5775 aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2019), 1 vom: 09. Dez. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2019 number:1 day:09 month:12 https://dx.doi.org/10.1007/s42452-019-1758-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 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_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_2007 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_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2019 1 09 12
allfieldsGer	10.1007/s42452-019-1758-8 doi (DE-627)SPR03858087X (SPR)s42452-019-1758-8-e DE-627 ger DE-627 rakwb eng Zain, Alneel Mohammed verfasserin aut Chemical reaction optimization (CRO) for cloud job scheduling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users’ submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimum scheduling solution, such as genetic algorithm, glowworm swarm optimization and cat swarm optimization. This study introduced an optimal metaheuristic job scheduling method using chemical reaction optimization (CRO). Chemical reaction optimization is a new metaheuristic algorithm inspired from the interactions of molecules to achieve the lowest energy state possible during a chemical reaction. CRO mimics molecules’ interaction in chemical reaction microscopically. The CRO mechanism simulates the interactions of chemical reaction molecules to find out the lowest energy value possible. The proposed mechanism represents the molecular structure of each molecule as a vector of integer values, each of which represents a feasible cloud job scheduling solution. The potential energy (%$PE%$) of each molecule represents its fitness in the objective function that denotes cloud scheduling execution time. Simulation using CloudSim simulator is used to evaluate the proposed CRO mechanism. A comparison with glowworm swarm optimization, cat swarm optimization and first come first served scheduling mechanism is made. The results of simulation showed that the CRO scheduling method has the shortest execution time among all other scheduling mechanisms. Chemical reaction optimization (dpeaa)DE-He213 Cloud computing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Metaheuristics (dpeaa)DE-He213 Yousif, Adil (orcid)0000-0002-7584-5775 aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2019), 1 vom: 09. Dez. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2019 number:1 day:09 month:12 https://dx.doi.org/10.1007/s42452-019-1758-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 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_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_2007 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_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2019 1 09 12
allfieldsSound	10.1007/s42452-019-1758-8 doi (DE-627)SPR03858087X (SPR)s42452-019-1758-8-e DE-627 ger DE-627 rakwb eng Zain, Alneel Mohammed verfasserin aut Chemical reaction optimization (CRO) for cloud job scheduling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2019 Abstract Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users’ submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimum scheduling solution, such as genetic algorithm, glowworm swarm optimization and cat swarm optimization. This study introduced an optimal metaheuristic job scheduling method using chemical reaction optimization (CRO). Chemical reaction optimization is a new metaheuristic algorithm inspired from the interactions of molecules to achieve the lowest energy state possible during a chemical reaction. CRO mimics molecules’ interaction in chemical reaction microscopically. The CRO mechanism simulates the interactions of chemical reaction molecules to find out the lowest energy value possible. The proposed mechanism represents the molecular structure of each molecule as a vector of integer values, each of which represents a feasible cloud job scheduling solution. The potential energy (%$PE%$) of each molecule represents its fitness in the objective function that denotes cloud scheduling execution time. Simulation using CloudSim simulator is used to evaluate the proposed CRO mechanism. A comparison with glowworm swarm optimization, cat swarm optimization and first come first served scheduling mechanism is made. The results of simulation showed that the CRO scheduling method has the shortest execution time among all other scheduling mechanisms. Chemical reaction optimization (dpeaa)DE-He213 Cloud computing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Metaheuristics (dpeaa)DE-He213 Yousif, Adil (orcid)0000-0002-7584-5775 aut Enthalten in SN applied sciences [Cham] : Springer International Publishing, 2019 2(2019), 1 vom: 09. Dez. (DE-627)103761139X (DE-600)2947292-1 2523-3971 nnns volume:2 year:2019 number:1 day:09 month:12 https://dx.doi.org/10.1007/s42452-019-1758-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 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_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_2007 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_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_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_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2019 1 09 12
language	English
source	Enthalten in SN applied sciences 2(2019), 1 vom: 09. Dez. volume:2 year:2019 number:1 day:09 month:12
sourceStr	Enthalten in SN applied sciences 2(2019), 1 vom: 09. Dez. volume:2 year:2019 number:1 day:09 month:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Chemical reaction optimization Cloud computing Scheduling Metaheuristics
isfreeaccess_bool	false
container_title	SN applied sciences
authorswithroles_txt_mv	Zain, Alneel Mohammed @@aut@@ Yousif, Adil @@aut@@
publishDateDaySort_date	2019-12-09T00:00:00Z
hierarchy_top_id	103761139X
id	SPR03858087X
language_de	englisch
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author	Zain, Alneel Mohammed
spellingShingle	Zain, Alneel Mohammed misc Chemical reaction optimization misc Cloud computing misc Scheduling misc Metaheuristics Chemical reaction optimization (CRO) for cloud job scheduling
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topic_title	Chemical reaction optimization (CRO) for cloud job scheduling Chemical reaction optimization (dpeaa)DE-He213 Cloud computing (dpeaa)DE-He213 Scheduling (dpeaa)DE-He213 Metaheuristics (dpeaa)DE-He213
topic	misc Chemical reaction optimization misc Cloud computing misc Scheduling misc Metaheuristics
topic_unstemmed	misc Chemical reaction optimization misc Cloud computing misc Scheduling misc Metaheuristics
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title	Chemical reaction optimization (CRO) for cloud job scheduling
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title_full	Chemical reaction optimization (CRO) for cloud job scheduling
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author_browse	Zain, Alneel Mohammed Yousif, Adil
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title_sort	chemical reaction optimization (cro) for cloud job scheduling
title_auth	Chemical reaction optimization (CRO) for cloud job scheduling
abstract	Abstract Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users’ submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimum scheduling solution, such as genetic algorithm, glowworm swarm optimization and cat swarm optimization. This study introduced an optimal metaheuristic job scheduling method using chemical reaction optimization (CRO). Chemical reaction optimization is a new metaheuristic algorithm inspired from the interactions of molecules to achieve the lowest energy state possible during a chemical reaction. CRO mimics molecules’ interaction in chemical reaction microscopically. The CRO mechanism simulates the interactions of chemical reaction molecules to find out the lowest energy value possible. The proposed mechanism represents the molecular structure of each molecule as a vector of integer values, each of which represents a feasible cloud job scheduling solution. The potential energy (%$PE%$) of each molecule represents its fitness in the objective function that denotes cloud scheduling execution time. Simulation using CloudSim simulator is used to evaluate the proposed CRO mechanism. A comparison with glowworm swarm optimization, cat swarm optimization and first come first served scheduling mechanism is made. The results of simulation showed that the CRO scheduling method has the shortest execution time among all other scheduling mechanisms. © Springer Nature Switzerland AG 2019
abstractGer	Abstract Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users’ submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimum scheduling solution, such as genetic algorithm, glowworm swarm optimization and cat swarm optimization. This study introduced an optimal metaheuristic job scheduling method using chemical reaction optimization (CRO). Chemical reaction optimization is a new metaheuristic algorithm inspired from the interactions of molecules to achieve the lowest energy state possible during a chemical reaction. CRO mimics molecules’ interaction in chemical reaction microscopically. The CRO mechanism simulates the interactions of chemical reaction molecules to find out the lowest energy value possible. The proposed mechanism represents the molecular structure of each molecule as a vector of integer values, each of which represents a feasible cloud job scheduling solution. The potential energy (%$PE%$) of each molecule represents its fitness in the objective function that denotes cloud scheduling execution time. Simulation using CloudSim simulator is used to evaluate the proposed CRO mechanism. A comparison with glowworm swarm optimization, cat swarm optimization and first come first served scheduling mechanism is made. The results of simulation showed that the CRO scheduling method has the shortest execution time among all other scheduling mechanisms. © Springer Nature Switzerland AG 2019
abstract_unstemmed	Abstract Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users’ submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimum scheduling solution, such as genetic algorithm, glowworm swarm optimization and cat swarm optimization. This study introduced an optimal metaheuristic job scheduling method using chemical reaction optimization (CRO). Chemical reaction optimization is a new metaheuristic algorithm inspired from the interactions of molecules to achieve the lowest energy state possible during a chemical reaction. CRO mimics molecules’ interaction in chemical reaction microscopically. The CRO mechanism simulates the interactions of chemical reaction molecules to find out the lowest energy value possible. The proposed mechanism represents the molecular structure of each molecule as a vector of integer values, each of which represents a feasible cloud job scheduling solution. The potential energy (%$PE%$) of each molecule represents its fitness in the objective function that denotes cloud scheduling execution time. Simulation using CloudSim simulator is used to evaluate the proposed CRO mechanism. A comparison with glowworm swarm optimization, cat swarm optimization and first come first served scheduling mechanism is made. The results of simulation showed that the CRO scheduling method has the shortest execution time among all other scheduling mechanisms. © Springer Nature Switzerland AG 2019
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title_short	Chemical reaction optimization (CRO) for cloud job scheduling
url	https://dx.doi.org/10.1007/s42452-019-1758-8
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doi_str	10.1007/s42452-019-1758-8
up_date	2024-07-03T18:58:52.414Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR03858087X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519145946.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s42452-019-1758-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR03858087X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s42452-019-1758-8-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="100" ind1="1" ind2=" "><subfield code="a">Zain, Alneel Mohammed</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Chemical reaction optimization (CRO) for cloud job scheduling</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</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="500" ind1=" " ind2=" "><subfield code="a">© Springer Nature Switzerland AG 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Cloud computing is an emerging technology that provides functions of traditional computing as services via the Internet. Cloud job scheduling mechanisms try to allocate cloud resources to users’ submitted jobs in an optimal way. Several metaheuristic algorithms are used to obtain the optimum scheduling solution, such as genetic algorithm, glowworm swarm optimization and cat swarm optimization. This study introduced an optimal metaheuristic job scheduling method using chemical reaction optimization (CRO). Chemical reaction optimization is a new metaheuristic algorithm inspired from the interactions of molecules to achieve the lowest energy state possible during a chemical reaction. CRO mimics molecules’ interaction in chemical reaction microscopically. The CRO mechanism simulates the interactions of chemical reaction molecules to find out the lowest energy value possible. The proposed mechanism represents the molecular structure of each molecule as a vector of integer values, each of which represents a feasible cloud job scheduling solution. The potential energy (%$PE%$) of each molecule represents its fitness in the objective function that denotes cloud scheduling execution time. Simulation using CloudSim simulator is used to evaluate the proposed CRO mechanism. A comparison with glowworm swarm optimization, cat swarm optimization and first come first served scheduling mechanism is made. 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Chemical reaction optimization (CRO) for cloud job scheduling

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