Enhancement in performance of cloud computing task scheduling using optimization strategies
Abstract Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This resea...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sandhu, Ramandeep [verfasserIn] Faiz, Mohammad [verfasserIn] Kaur, Harpreet [verfasserIn] Srivastava, Ashish [verfasserIn] Narayan, Vipul [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Cluster computing - Springer US, 1998, 27(2024), 5 vom: 27. Feb., Seite 6265-6288 |
|---|---|
| Übergeordnetes Werk: |
volume:27 ; year:2024 ; number:5 ; day:27 ; month:02 ; pages:6265-6288 |
| Links: |
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| DOI / URN: |
10.1007/s10586-023-04254-w |
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| Katalog-ID: |
SPR056936680 |
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| 520 | |a Abstract Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This research offers a more effective method for using optimization techniques to improve the efficiency of cloud computing task scheduling. Data centers, hosts, and virtual machines (VMs) comprise cloud infrastructures, and work scheduling is crucial to achieving peak performance. To save time, money, energy, and reaction times, scheduling must be done effectively; the primary objective of this research is to develop and evaluate optimization techniques for task scheduling in cloud environments. The following goals are prioritized in the proposed work: (i) reducing the Total Execution Cost (TEC) of the scheduling process; (ii) reducing the Total Execution Time (TET) during mapping; (iii) achieving appropriate task-to-VM mapping to reduce Energy Consumption (EC); and (iv) reducing the overall Response Time (RT) of the cloud scheduling system. To accomplish these objectives, we offer a method based on the use of three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W). Our experimental findings show that, in terms of accomplishing the targeted objectives, the suggested TBW optimization methodology outperforms more well-known approaches like GA-PSO and Whale Optimization. By offering insights into efficient resource usage techniques and overall system effectiveness by 95% for the range of 8 to 14 VMs, this work helps ongoing attempts to improve the performance of cloud computing. | ||
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| 700 | 1 | |a Srivastava, Ashish |e verfasserin |4 aut | |
| 700 | 1 | |a Narayan, Vipul |e verfasserin |4 aut | |
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10.1007/s10586-023-04254-w doi (DE-627)SPR056936680 (SPR)s10586-023-04254-w-e DE-627 ger DE-627 rakwb eng 004 VZ 54.50 bkl 54.32 bkl 54.25 bkl Sandhu, Ramandeep verfasserin aut Enhancement in performance of cloud computing task scheduling using optimization strategies 2024 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 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This research offers a more effective method for using optimization techniques to improve the efficiency of cloud computing task scheduling. Data centers, hosts, and virtual machines (VMs) comprise cloud infrastructures, and work scheduling is crucial to achieving peak performance. To save time, money, energy, and reaction times, scheduling must be done effectively; the primary objective of this research is to develop and evaluate optimization techniques for task scheduling in cloud environments. The following goals are prioritized in the proposed work: (i) reducing the Total Execution Cost (TEC) of the scheduling process; (ii) reducing the Total Execution Time (TET) during mapping; (iii) achieving appropriate task-to-VM mapping to reduce Energy Consumption (EC); and (iv) reducing the overall Response Time (RT) of the cloud scheduling system. To accomplish these objectives, we offer a method based on the use of three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W). Our experimental findings show that, in terms of accomplishing the targeted objectives, the suggested TBW optimization methodology outperforms more well-known approaches like GA-PSO and Whale Optimization. By offering insights into efficient resource usage techniques and overall system effectiveness by 95% for the range of 8 to 14 VMs, this work helps ongoing attempts to improve the performance of cloud computing. Scientific workflow (dpeaa)DE-He213 Workflow scheduling (dpeaa)DE-He213 Cloud optimization (dpeaa)DE-He213 Tabu search (dpeaa)DE-He213 Bayesian classification (dpeaa)DE-He213 Whale optimization (dpeaa)DE-He213 TBW optimization (dpeaa)DE-He213 Faiz, Mohammad verfasserin aut Kaur, Harpreet verfasserin aut Srivastava, Ashish verfasserin aut Narayan, Vipul verfasserin aut Enthalten in Cluster computing Springer US, 1998 27(2024), 5 vom: 27. Feb., Seite 6265-6288 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2024 number:5 day:27 month:02 pages:6265-6288 https://dx.doi.org/10.1007/s10586-023-04254-w X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_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 54.50 VZ 54.32 VZ 54.25 VZ AR 27 2024 5 27 02 6265-6288 |
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10.1007/s10586-023-04254-w doi (DE-627)SPR056936680 (SPR)s10586-023-04254-w-e DE-627 ger DE-627 rakwb eng 004 VZ 54.50 bkl 54.32 bkl 54.25 bkl Sandhu, Ramandeep verfasserin aut Enhancement in performance of cloud computing task scheduling using optimization strategies 2024 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 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This research offers a more effective method for using optimization techniques to improve the efficiency of cloud computing task scheduling. Data centers, hosts, and virtual machines (VMs) comprise cloud infrastructures, and work scheduling is crucial to achieving peak performance. To save time, money, energy, and reaction times, scheduling must be done effectively; the primary objective of this research is to develop and evaluate optimization techniques for task scheduling in cloud environments. The following goals are prioritized in the proposed work: (i) reducing the Total Execution Cost (TEC) of the scheduling process; (ii) reducing the Total Execution Time (TET) during mapping; (iii) achieving appropriate task-to-VM mapping to reduce Energy Consumption (EC); and (iv) reducing the overall Response Time (RT) of the cloud scheduling system. To accomplish these objectives, we offer a method based on the use of three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W). Our experimental findings show that, in terms of accomplishing the targeted objectives, the suggested TBW optimization methodology outperforms more well-known approaches like GA-PSO and Whale Optimization. By offering insights into efficient resource usage techniques and overall system effectiveness by 95% for the range of 8 to 14 VMs, this work helps ongoing attempts to improve the performance of cloud computing. Scientific workflow (dpeaa)DE-He213 Workflow scheduling (dpeaa)DE-He213 Cloud optimization (dpeaa)DE-He213 Tabu search (dpeaa)DE-He213 Bayesian classification (dpeaa)DE-He213 Whale optimization (dpeaa)DE-He213 TBW optimization (dpeaa)DE-He213 Faiz, Mohammad verfasserin aut Kaur, Harpreet verfasserin aut Srivastava, Ashish verfasserin aut Narayan, Vipul verfasserin aut Enthalten in Cluster computing Springer US, 1998 27(2024), 5 vom: 27. Feb., Seite 6265-6288 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2024 number:5 day:27 month:02 pages:6265-6288 https://dx.doi.org/10.1007/s10586-023-04254-w X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_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 54.50 VZ 54.32 VZ 54.25 VZ AR 27 2024 5 27 02 6265-6288 |
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10.1007/s10586-023-04254-w doi (DE-627)SPR056936680 (SPR)s10586-023-04254-w-e DE-627 ger DE-627 rakwb eng 004 VZ 54.50 bkl 54.32 bkl 54.25 bkl Sandhu, Ramandeep verfasserin aut Enhancement in performance of cloud computing task scheduling using optimization strategies 2024 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 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This research offers a more effective method for using optimization techniques to improve the efficiency of cloud computing task scheduling. Data centers, hosts, and virtual machines (VMs) comprise cloud infrastructures, and work scheduling is crucial to achieving peak performance. To save time, money, energy, and reaction times, scheduling must be done effectively; the primary objective of this research is to develop and evaluate optimization techniques for task scheduling in cloud environments. The following goals are prioritized in the proposed work: (i) reducing the Total Execution Cost (TEC) of the scheduling process; (ii) reducing the Total Execution Time (TET) during mapping; (iii) achieving appropriate task-to-VM mapping to reduce Energy Consumption (EC); and (iv) reducing the overall Response Time (RT) of the cloud scheduling system. To accomplish these objectives, we offer a method based on the use of three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W). Our experimental findings show that, in terms of accomplishing the targeted objectives, the suggested TBW optimization methodology outperforms more well-known approaches like GA-PSO and Whale Optimization. By offering insights into efficient resource usage techniques and overall system effectiveness by 95% for the range of 8 to 14 VMs, this work helps ongoing attempts to improve the performance of cloud computing. Scientific workflow (dpeaa)DE-He213 Workflow scheduling (dpeaa)DE-He213 Cloud optimization (dpeaa)DE-He213 Tabu search (dpeaa)DE-He213 Bayesian classification (dpeaa)DE-He213 Whale optimization (dpeaa)DE-He213 TBW optimization (dpeaa)DE-He213 Faiz, Mohammad verfasserin aut Kaur, Harpreet verfasserin aut Srivastava, Ashish verfasserin aut Narayan, Vipul verfasserin aut Enthalten in Cluster computing Springer US, 1998 27(2024), 5 vom: 27. Feb., Seite 6265-6288 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2024 number:5 day:27 month:02 pages:6265-6288 https://dx.doi.org/10.1007/s10586-023-04254-w X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_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 54.50 VZ 54.32 VZ 54.25 VZ AR 27 2024 5 27 02 6265-6288 |
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10.1007/s10586-023-04254-w doi (DE-627)SPR056936680 (SPR)s10586-023-04254-w-e DE-627 ger DE-627 rakwb eng 004 VZ 54.50 bkl 54.32 bkl 54.25 bkl Sandhu, Ramandeep verfasserin aut Enhancement in performance of cloud computing task scheduling using optimization strategies 2024 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 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This research offers a more effective method for using optimization techniques to improve the efficiency of cloud computing task scheduling. Data centers, hosts, and virtual machines (VMs) comprise cloud infrastructures, and work scheduling is crucial to achieving peak performance. To save time, money, energy, and reaction times, scheduling must be done effectively; the primary objective of this research is to develop and evaluate optimization techniques for task scheduling in cloud environments. The following goals are prioritized in the proposed work: (i) reducing the Total Execution Cost (TEC) of the scheduling process; (ii) reducing the Total Execution Time (TET) during mapping; (iii) achieving appropriate task-to-VM mapping to reduce Energy Consumption (EC); and (iv) reducing the overall Response Time (RT) of the cloud scheduling system. To accomplish these objectives, we offer a method based on the use of three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W). Our experimental findings show that, in terms of accomplishing the targeted objectives, the suggested TBW optimization methodology outperforms more well-known approaches like GA-PSO and Whale Optimization. By offering insights into efficient resource usage techniques and overall system effectiveness by 95% for the range of 8 to 14 VMs, this work helps ongoing attempts to improve the performance of cloud computing. Scientific workflow (dpeaa)DE-He213 Workflow scheduling (dpeaa)DE-He213 Cloud optimization (dpeaa)DE-He213 Tabu search (dpeaa)DE-He213 Bayesian classification (dpeaa)DE-He213 Whale optimization (dpeaa)DE-He213 TBW optimization (dpeaa)DE-He213 Faiz, Mohammad verfasserin aut Kaur, Harpreet verfasserin aut Srivastava, Ashish verfasserin aut Narayan, Vipul verfasserin aut Enthalten in Cluster computing Springer US, 1998 27(2024), 5 vom: 27. Feb., Seite 6265-6288 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2024 number:5 day:27 month:02 pages:6265-6288 https://dx.doi.org/10.1007/s10586-023-04254-w X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_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 54.50 VZ 54.32 VZ 54.25 VZ AR 27 2024 5 27 02 6265-6288 |
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10.1007/s10586-023-04254-w doi (DE-627)SPR056936680 (SPR)s10586-023-04254-w-e DE-627 ger DE-627 rakwb eng 004 VZ 54.50 bkl 54.32 bkl 54.25 bkl Sandhu, Ramandeep verfasserin aut Enhancement in performance of cloud computing task scheduling using optimization strategies 2024 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 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This research offers a more effective method for using optimization techniques to improve the efficiency of cloud computing task scheduling. Data centers, hosts, and virtual machines (VMs) comprise cloud infrastructures, and work scheduling is crucial to achieving peak performance. To save time, money, energy, and reaction times, scheduling must be done effectively; the primary objective of this research is to develop and evaluate optimization techniques for task scheduling in cloud environments. The following goals are prioritized in the proposed work: (i) reducing the Total Execution Cost (TEC) of the scheduling process; (ii) reducing the Total Execution Time (TET) during mapping; (iii) achieving appropriate task-to-VM mapping to reduce Energy Consumption (EC); and (iv) reducing the overall Response Time (RT) of the cloud scheduling system. To accomplish these objectives, we offer a method based on the use of three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W). Our experimental findings show that, in terms of accomplishing the targeted objectives, the suggested TBW optimization methodology outperforms more well-known approaches like GA-PSO and Whale Optimization. By offering insights into efficient resource usage techniques and overall system effectiveness by 95% for the range of 8 to 14 VMs, this work helps ongoing attempts to improve the performance of cloud computing. Scientific workflow (dpeaa)DE-He213 Workflow scheduling (dpeaa)DE-He213 Cloud optimization (dpeaa)DE-He213 Tabu search (dpeaa)DE-He213 Bayesian classification (dpeaa)DE-He213 Whale optimization (dpeaa)DE-He213 TBW optimization (dpeaa)DE-He213 Faiz, Mohammad verfasserin aut Kaur, Harpreet verfasserin aut Srivastava, Ashish verfasserin aut Narayan, Vipul verfasserin aut Enthalten in Cluster computing Springer US, 1998 27(2024), 5 vom: 27. Feb., Seite 6265-6288 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2024 number:5 day:27 month:02 pages:6265-6288 https://dx.doi.org/10.1007/s10586-023-04254-w X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 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_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_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 54.50 VZ 54.32 VZ 54.25 VZ AR 27 2024 5 27 02 6265-6288 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This research offers a more effective method for using optimization techniques to improve the efficiency of cloud computing task scheduling. Data centers, hosts, and virtual machines (VMs) comprise cloud infrastructures, and work scheduling is crucial to achieving peak performance. 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Enhancement in performance of cloud computing task scheduling using optimization strategies |
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Abstract Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This research offers a more effective method for using optimization techniques to improve the efficiency of cloud computing task scheduling. Data centers, hosts, and virtual machines (VMs) comprise cloud infrastructures, and work scheduling is crucial to achieving peak performance. To save time, money, energy, and reaction times, scheduling must be done effectively; the primary objective of this research is to develop and evaluate optimization techniques for task scheduling in cloud environments. The following goals are prioritized in the proposed work: (i) reducing the Total Execution Cost (TEC) of the scheduling process; (ii) reducing the Total Execution Time (TET) during mapping; (iii) achieving appropriate task-to-VM mapping to reduce Energy Consumption (EC); and (iv) reducing the overall Response Time (RT) of the cloud scheduling system. To accomplish these objectives, we offer a method based on the use of three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W). Our experimental findings show that, in terms of accomplishing the targeted objectives, the suggested TBW optimization methodology outperforms more well-known approaches like GA-PSO and Whale Optimization. By offering insights into efficient resource usage techniques and overall system effectiveness by 95% for the range of 8 to 14 VMs, this work helps ongoing attempts to improve the performance of cloud computing. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This research offers a more effective method for using optimization techniques to improve the efficiency of cloud computing task scheduling. Data centers, hosts, and virtual machines (VMs) comprise cloud infrastructures, and work scheduling is crucial to achieving peak performance. To save time, money, energy, and reaction times, scheduling must be done effectively; the primary objective of this research is to develop and evaluate optimization techniques for task scheduling in cloud environments. The following goals are prioritized in the proposed work: (i) reducing the Total Execution Cost (TEC) of the scheduling process; (ii) reducing the Total Execution Time (TET) during mapping; (iii) achieving appropriate task-to-VM mapping to reduce Energy Consumption (EC); and (iv) reducing the overall Response Time (RT) of the cloud scheduling system. To accomplish these objectives, we offer a method based on the use of three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W). Our experimental findings show that, in terms of accomplishing the targeted objectives, the suggested TBW optimization methodology outperforms more well-known approaches like GA-PSO and Whale Optimization. By offering insights into efficient resource usage techniques and overall system effectiveness by 95% for the range of 8 to 14 VMs, this work helps ongoing attempts to improve the performance of cloud computing. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This research offers a more effective method for using optimization techniques to improve the efficiency of cloud computing task scheduling. Data centers, hosts, and virtual machines (VMs) comprise cloud infrastructures, and work scheduling is crucial to achieving peak performance. To save time, money, energy, and reaction times, scheduling must be done effectively; the primary objective of this research is to develop and evaluate optimization techniques for task scheduling in cloud environments. The following goals are prioritized in the proposed work: (i) reducing the Total Execution Cost (TEC) of the scheduling process; (ii) reducing the Total Execution Time (TET) during mapping; (iii) achieving appropriate task-to-VM mapping to reduce Energy Consumption (EC); and (iv) reducing the overall Response Time (RT) of the cloud scheduling system. To accomplish these objectives, we offer a method based on the use of three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W). Our experimental findings show that, in terms of accomplishing the targeted objectives, the suggested TBW optimization methodology outperforms more well-known approaches like GA-PSO and Whale Optimization. By offering insights into efficient resource usage techniques and overall system effectiveness by 95% for the range of 8 to 14 VMs, this work helps ongoing attempts to improve the performance of cloud computing. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| score |
7.4018965 |