A Workflow Scheduling Method for Cloud Computing Platform

Abstract Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs an optimal method that decides how to allocate and execute the tasks on a computing platform, so, it generates an efficient resu...
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Autor*in:	Rajak, Nidhi [verfasserIn] Rajak, Ranjit Prakash, Shiv

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Workflow DAG BFS Makespan Speedup Virtual Machine Cost

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Wireless personal communications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 126(2022), 4 vom: 22. Juni, Seite 3625-3647
Übergeordnetes Werk:	volume:126 ; year:2022 ; number:4 ; day:22 ; month:06 ; pages:3625-3647

Links:	Volltext

DOI / URN:	10.1007/s11277-022-09882-w

Katalog-ID:	SPR048400114

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allfields	10.1007/s11277-022-09882-w doi (DE-627)SPR048400114 (SPR)s11277-022-09882-w-e DE-627 ger DE-627 rakwb eng Rajak, Nidhi verfasserin aut A Workflow Scheduling Method for Cloud Computing Platform 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs an optimal method that decides how to allocate and execute the tasks on a computing platform, so, it generates an efficient result. Here, the tasks are connected to each other and depicted using DAG which is extensively used in Cloud Scheduling modeling. Generally, cloud work on principal pay per resources uses basis. This paper presents a new scheme for scheduling of the tasks in a cloud platform. The proposed algorithm uses heuristic-guided Breadth-First Search (BFS) which works on two steps process as first it the priority computation of the tasks and second is to assign these tasks to the available virtual machines with an entry task as duplicate to all virtual machines. This leads to reducing the scheduling length of the task scheduling and it is the prime with workflow scheduling algorithm. This paper also discussed performance analysis of the new method with the heuristic algorithms using various well known metrics. The proposed method gives better results than the state of the art. Workflow (dpeaa)DE-He213 DAG (dpeaa)DE-He213 BFS (dpeaa)DE-He213 Makespan (dpeaa)DE-He213 Speedup (dpeaa)DE-He213 Virtual Machine (dpeaa)DE-He213 Cost (dpeaa)DE-He213 Rajak, Ranjit (orcid)0000-0003-2746-3278 aut Prakash, Shiv aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 126(2022), 4 vom: 22. Juni, Seite 3625-3647 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:126 year:2022 number:4 day:22 month:06 pages:3625-3647 https://dx.doi.org/10.1007/s11277-022-09882-w 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 126 2022 4 22 06 3625-3647
spelling	10.1007/s11277-022-09882-w doi (DE-627)SPR048400114 (SPR)s11277-022-09882-w-e DE-627 ger DE-627 rakwb eng Rajak, Nidhi verfasserin aut A Workflow Scheduling Method for Cloud Computing Platform 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs an optimal method that decides how to allocate and execute the tasks on a computing platform, so, it generates an efficient result. Here, the tasks are connected to each other and depicted using DAG which is extensively used in Cloud Scheduling modeling. Generally, cloud work on principal pay per resources uses basis. This paper presents a new scheme for scheduling of the tasks in a cloud platform. The proposed algorithm uses heuristic-guided Breadth-First Search (BFS) which works on two steps process as first it the priority computation of the tasks and second is to assign these tasks to the available virtual machines with an entry task as duplicate to all virtual machines. This leads to reducing the scheduling length of the task scheduling and it is the prime with workflow scheduling algorithm. This paper also discussed performance analysis of the new method with the heuristic algorithms using various well known metrics. The proposed method gives better results than the state of the art. Workflow (dpeaa)DE-He213 DAG (dpeaa)DE-He213 BFS (dpeaa)DE-He213 Makespan (dpeaa)DE-He213 Speedup (dpeaa)DE-He213 Virtual Machine (dpeaa)DE-He213 Cost (dpeaa)DE-He213 Rajak, Ranjit (orcid)0000-0003-2746-3278 aut Prakash, Shiv aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 126(2022), 4 vom: 22. Juni, Seite 3625-3647 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:126 year:2022 number:4 day:22 month:06 pages:3625-3647 https://dx.doi.org/10.1007/s11277-022-09882-w 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 126 2022 4 22 06 3625-3647
allfields_unstemmed	10.1007/s11277-022-09882-w doi (DE-627)SPR048400114 (SPR)s11277-022-09882-w-e DE-627 ger DE-627 rakwb eng Rajak, Nidhi verfasserin aut A Workflow Scheduling Method for Cloud Computing Platform 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs an optimal method that decides how to allocate and execute the tasks on a computing platform, so, it generates an efficient result. Here, the tasks are connected to each other and depicted using DAG which is extensively used in Cloud Scheduling modeling. Generally, cloud work on principal pay per resources uses basis. This paper presents a new scheme for scheduling of the tasks in a cloud platform. The proposed algorithm uses heuristic-guided Breadth-First Search (BFS) which works on two steps process as first it the priority computation of the tasks and second is to assign these tasks to the available virtual machines with an entry task as duplicate to all virtual machines. This leads to reducing the scheduling length of the task scheduling and it is the prime with workflow scheduling algorithm. This paper also discussed performance analysis of the new method with the heuristic algorithms using various well known metrics. The proposed method gives better results than the state of the art. Workflow (dpeaa)DE-He213 DAG (dpeaa)DE-He213 BFS (dpeaa)DE-He213 Makespan (dpeaa)DE-He213 Speedup (dpeaa)DE-He213 Virtual Machine (dpeaa)DE-He213 Cost (dpeaa)DE-He213 Rajak, Ranjit (orcid)0000-0003-2746-3278 aut Prakash, Shiv aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 126(2022), 4 vom: 22. Juni, Seite 3625-3647 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:126 year:2022 number:4 day:22 month:06 pages:3625-3647 https://dx.doi.org/10.1007/s11277-022-09882-w 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 126 2022 4 22 06 3625-3647
allfieldsGer	10.1007/s11277-022-09882-w doi (DE-627)SPR048400114 (SPR)s11277-022-09882-w-e DE-627 ger DE-627 rakwb eng Rajak, Nidhi verfasserin aut A Workflow Scheduling Method for Cloud Computing Platform 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs an optimal method that decides how to allocate and execute the tasks on a computing platform, so, it generates an efficient result. Here, the tasks are connected to each other and depicted using DAG which is extensively used in Cloud Scheduling modeling. Generally, cloud work on principal pay per resources uses basis. This paper presents a new scheme for scheduling of the tasks in a cloud platform. The proposed algorithm uses heuristic-guided Breadth-First Search (BFS) which works on two steps process as first it the priority computation of the tasks and second is to assign these tasks to the available virtual machines with an entry task as duplicate to all virtual machines. This leads to reducing the scheduling length of the task scheduling and it is the prime with workflow scheduling algorithm. This paper also discussed performance analysis of the new method with the heuristic algorithms using various well known metrics. The proposed method gives better results than the state of the art. Workflow (dpeaa)DE-He213 DAG (dpeaa)DE-He213 BFS (dpeaa)DE-He213 Makespan (dpeaa)DE-He213 Speedup (dpeaa)DE-He213 Virtual Machine (dpeaa)DE-He213 Cost (dpeaa)DE-He213 Rajak, Ranjit (orcid)0000-0003-2746-3278 aut Prakash, Shiv aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 126(2022), 4 vom: 22. Juni, Seite 3625-3647 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:126 year:2022 number:4 day:22 month:06 pages:3625-3647 https://dx.doi.org/10.1007/s11277-022-09882-w 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 126 2022 4 22 06 3625-3647
allfieldsSound	10.1007/s11277-022-09882-w doi (DE-627)SPR048400114 (SPR)s11277-022-09882-w-e DE-627 ger DE-627 rakwb eng Rajak, Nidhi verfasserin aut A Workflow Scheduling Method for Cloud Computing Platform 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs an optimal method that decides how to allocate and execute the tasks on a computing platform, so, it generates an efficient result. Here, the tasks are connected to each other and depicted using DAG which is extensively used in Cloud Scheduling modeling. Generally, cloud work on principal pay per resources uses basis. This paper presents a new scheme for scheduling of the tasks in a cloud platform. The proposed algorithm uses heuristic-guided Breadth-First Search (BFS) which works on two steps process as first it the priority computation of the tasks and second is to assign these tasks to the available virtual machines with an entry task as duplicate to all virtual machines. This leads to reducing the scheduling length of the task scheduling and it is the prime with workflow scheduling algorithm. This paper also discussed performance analysis of the new method with the heuristic algorithms using various well known metrics. The proposed method gives better results than the state of the art. Workflow (dpeaa)DE-He213 DAG (dpeaa)DE-He213 BFS (dpeaa)DE-He213 Makespan (dpeaa)DE-He213 Speedup (dpeaa)DE-He213 Virtual Machine (dpeaa)DE-He213 Cost (dpeaa)DE-He213 Rajak, Ranjit (orcid)0000-0003-2746-3278 aut Prakash, Shiv aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 126(2022), 4 vom: 22. Juni, Seite 3625-3647 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:126 year:2022 number:4 day:22 month:06 pages:3625-3647 https://dx.doi.org/10.1007/s11277-022-09882-w 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 126 2022 4 22 06 3625-3647
language	English
source	Enthalten in Wireless personal communications 126(2022), 4 vom: 22. Juni, Seite 3625-3647 volume:126 year:2022 number:4 day:22 month:06 pages:3625-3647
sourceStr	Enthalten in Wireless personal communications 126(2022), 4 vom: 22. Juni, Seite 3625-3647 volume:126 year:2022 number:4 day:22 month:06 pages:3625-3647
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Workflow DAG BFS Makespan Speedup Virtual Machine Cost
isfreeaccess_bool	false
container_title	Wireless personal communications
authorswithroles_txt_mv	Rajak, Nidhi @@aut@@ Rajak, Ranjit @@aut@@ Prakash, Shiv @@aut@@
publishDateDaySort_date	2022-06-22T00:00:00Z
hierarchy_top_id	271179120
id	SPR048400114
language_de	englisch
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author	Rajak, Nidhi
spellingShingle	Rajak, Nidhi misc Workflow misc DAG misc BFS misc Makespan misc Speedup misc Virtual Machine misc Cost A Workflow Scheduling Method for Cloud Computing Platform
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topic_title	A Workflow Scheduling Method for Cloud Computing Platform Workflow (dpeaa)DE-He213 DAG (dpeaa)DE-He213 BFS (dpeaa)DE-He213 Makespan (dpeaa)DE-He213 Speedup (dpeaa)DE-He213 Virtual Machine (dpeaa)DE-He213 Cost (dpeaa)DE-He213
topic	misc Workflow misc DAG misc BFS misc Makespan misc Speedup misc Virtual Machine misc Cost
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title	A Workflow Scheduling Method for Cloud Computing Platform
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title_sort	workflow scheduling method for cloud computing platform
title_auth	A Workflow Scheduling Method for Cloud Computing Platform
abstract	Abstract Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs an optimal method that decides how to allocate and execute the tasks on a computing platform, so, it generates an efficient result. Here, the tasks are connected to each other and depicted using DAG which is extensively used in Cloud Scheduling modeling. Generally, cloud work on principal pay per resources uses basis. This paper presents a new scheme for scheduling of the tasks in a cloud platform. The proposed algorithm uses heuristic-guided Breadth-First Search (BFS) which works on two steps process as first it the priority computation of the tasks and second is to assign these tasks to the available virtual machines with an entry task as duplicate to all virtual machines. This leads to reducing the scheduling length of the task scheduling and it is the prime with workflow scheduling algorithm. This paper also discussed performance analysis of the new method with the heuristic algorithms using various well known metrics. The proposed method gives better results than the state of the art. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstractGer	Abstract Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs an optimal method that decides how to allocate and execute the tasks on a computing platform, so, it generates an efficient result. Here, the tasks are connected to each other and depicted using DAG which is extensively used in Cloud Scheduling modeling. Generally, cloud work on principal pay per resources uses basis. This paper presents a new scheme for scheduling of the tasks in a cloud platform. The proposed algorithm uses heuristic-guided Breadth-First Search (BFS) which works on two steps process as first it the priority computation of the tasks and second is to assign these tasks to the available virtual machines with an entry task as duplicate to all virtual machines. This leads to reducing the scheduling length of the task scheduling and it is the prime with workflow scheduling algorithm. This paper also discussed performance analysis of the new method with the heuristic algorithms using various well known metrics. The proposed method gives better results than the state of the art. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstract_unstemmed	Abstract Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs an optimal method that decides how to allocate and execute the tasks on a computing platform, so, it generates an efficient result. Here, the tasks are connected to each other and depicted using DAG which is extensively used in Cloud Scheduling modeling. Generally, cloud work on principal pay per resources uses basis. This paper presents a new scheme for scheduling of the tasks in a cloud platform. The proposed algorithm uses heuristic-guided Breadth-First Search (BFS) which works on two steps process as first it the priority computation of the tasks and second is to assign these tasks to the available virtual machines with an entry task as duplicate to all virtual machines. This leads to reducing the scheduling length of the task scheduling and it is the prime with workflow scheduling algorithm. This paper also discussed performance analysis of the new method with the heuristic algorithms using various well known metrics. The proposed method gives better results than the state of the art. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
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container_issue	4
title_short	A Workflow Scheduling Method for Cloud Computing Platform
url	https://dx.doi.org/10.1007/s11277-022-09882-w
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author2	Rajak, Ranjit Prakash, Shiv
author2Str	Rajak, Ranjit Prakash, Shiv
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doi_str	10.1007/s11277-022-09882-w
up_date	2024-07-03T18:57:49.685Z
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