A Distributed and Elastic Application Processing Model for Mobile Cloud Computing
Abstract The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shiraz, Muhammad [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Springer US, 1994, 95(2017), 4 vom: 13. März, Seite 4403-4423 |
|---|---|
| Übergeordnetes Werk: |
volume:95 ; year:2017 ; number:4 ; day:13 ; month:03 ; pages:4403-4423 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11277-017-4086-6 |
|---|
| Katalog-ID: |
OLC2053807172 |
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| 520 | |a Abstract The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources and powerful computational cloud is tapped for enabling intensive applications on SMDs. In Mobile Cloud Computing (MCC), application processing services of computational clouds are leveraged for alleviating resource limitations in SMDs. The particular deficiency of distributed architecture and runtime partitioning of the elastic mobile application are the challenging aspects of current offloading models. To address these issues of traditional models for computational offloading in MCC, this paper proposes a novel distributed and elastic applications processing (DEAP) model for intensive applications in MCC. We present an analytical model to evaluate the proposed DEAP model, and test a prototype application in the real MCC environment to demonstrate the usefulness of DEAP model. Computational offloading using the DEAP model minimizes resources utilization on SMD in the distributed processing of intensive mobile applications. Evaluation indicates a reduction of 74.6% in the overhead of runtime application partitioning and a 66.6% reduction in the CPU utilization for the execution of the application on SMD. | ||
| 650 | 4 | |a Application processing model | |
| 650 | 4 | |a Mobile Cloud Computing | |
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| 650 | 4 | |a Distributed application | |
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| 700 | 1 | |a Chilamkurti, Naveen |4 aut | |
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10.1007/s11277-017-4086-6 doi (DE-627)OLC2053807172 (DE-He213)s11277-017-4086-6-p DE-627 ger DE-627 rakwb eng 620 VZ Shiraz, Muhammad verfasserin aut A Distributed and Elastic Application Processing Model for Mobile Cloud Computing 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources and powerful computational cloud is tapped for enabling intensive applications on SMDs. In Mobile Cloud Computing (MCC), application processing services of computational clouds are leveraged for alleviating resource limitations in SMDs. The particular deficiency of distributed architecture and runtime partitioning of the elastic mobile application are the challenging aspects of current offloading models. To address these issues of traditional models for computational offloading in MCC, this paper proposes a novel distributed and elastic applications processing (DEAP) model for intensive applications in MCC. We present an analytical model to evaluate the proposed DEAP model, and test a prototype application in the real MCC environment to demonstrate the usefulness of DEAP model. Computational offloading using the DEAP model minimizes resources utilization on SMD in the distributed processing of intensive mobile applications. Evaluation indicates a reduction of 74.6% in the overhead of runtime application partitioning and a 66.6% reduction in the CPU utilization for the execution of the application on SMD. Application processing model Mobile Cloud Computing Smart mobile devices Distributed application Gani, Abdullah aut Khokhar, Rashid aut Rahman, Azizur aut Iftikhar, Mohsin (orcid)0000-0003-4736-0671 aut Chilamkurti, Naveen aut Enthalten in Wireless personal communications Springer US, 1994 95(2017), 4 vom: 13. März, Seite 4403-4423 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:95 year:2017 number:4 day:13 month:03 pages:4403-4423 https://doi.org/10.1007/s11277-017-4086-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 95 2017 4 13 03 4403-4423 |
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10.1007/s11277-017-4086-6 doi (DE-627)OLC2053807172 (DE-He213)s11277-017-4086-6-p DE-627 ger DE-627 rakwb eng 620 VZ Shiraz, Muhammad verfasserin aut A Distributed and Elastic Application Processing Model for Mobile Cloud Computing 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources and powerful computational cloud is tapped for enabling intensive applications on SMDs. In Mobile Cloud Computing (MCC), application processing services of computational clouds are leveraged for alleviating resource limitations in SMDs. The particular deficiency of distributed architecture and runtime partitioning of the elastic mobile application are the challenging aspects of current offloading models. To address these issues of traditional models for computational offloading in MCC, this paper proposes a novel distributed and elastic applications processing (DEAP) model for intensive applications in MCC. We present an analytical model to evaluate the proposed DEAP model, and test a prototype application in the real MCC environment to demonstrate the usefulness of DEAP model. Computational offloading using the DEAP model minimizes resources utilization on SMD in the distributed processing of intensive mobile applications. Evaluation indicates a reduction of 74.6% in the overhead of runtime application partitioning and a 66.6% reduction in the CPU utilization for the execution of the application on SMD. Application processing model Mobile Cloud Computing Smart mobile devices Distributed application Gani, Abdullah aut Khokhar, Rashid aut Rahman, Azizur aut Iftikhar, Mohsin (orcid)0000-0003-4736-0671 aut Chilamkurti, Naveen aut Enthalten in Wireless personal communications Springer US, 1994 95(2017), 4 vom: 13. März, Seite 4403-4423 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:95 year:2017 number:4 day:13 month:03 pages:4403-4423 https://doi.org/10.1007/s11277-017-4086-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 95 2017 4 13 03 4403-4423 |
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10.1007/s11277-017-4086-6 doi (DE-627)OLC2053807172 (DE-He213)s11277-017-4086-6-p DE-627 ger DE-627 rakwb eng 620 VZ Shiraz, Muhammad verfasserin aut A Distributed and Elastic Application Processing Model for Mobile Cloud Computing 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources and powerful computational cloud is tapped for enabling intensive applications on SMDs. In Mobile Cloud Computing (MCC), application processing services of computational clouds are leveraged for alleviating resource limitations in SMDs. The particular deficiency of distributed architecture and runtime partitioning of the elastic mobile application are the challenging aspects of current offloading models. To address these issues of traditional models for computational offloading in MCC, this paper proposes a novel distributed and elastic applications processing (DEAP) model for intensive applications in MCC. We present an analytical model to evaluate the proposed DEAP model, and test a prototype application in the real MCC environment to demonstrate the usefulness of DEAP model. Computational offloading using the DEAP model minimizes resources utilization on SMD in the distributed processing of intensive mobile applications. Evaluation indicates a reduction of 74.6% in the overhead of runtime application partitioning and a 66.6% reduction in the CPU utilization for the execution of the application on SMD. Application processing model Mobile Cloud Computing Smart mobile devices Distributed application Gani, Abdullah aut Khokhar, Rashid aut Rahman, Azizur aut Iftikhar, Mohsin (orcid)0000-0003-4736-0671 aut Chilamkurti, Naveen aut Enthalten in Wireless personal communications Springer US, 1994 95(2017), 4 vom: 13. März, Seite 4403-4423 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:95 year:2017 number:4 day:13 month:03 pages:4403-4423 https://doi.org/10.1007/s11277-017-4086-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 95 2017 4 13 03 4403-4423 |
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10.1007/s11277-017-4086-6 doi (DE-627)OLC2053807172 (DE-He213)s11277-017-4086-6-p DE-627 ger DE-627 rakwb eng 620 VZ Shiraz, Muhammad verfasserin aut A Distributed and Elastic Application Processing Model for Mobile Cloud Computing 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2017 Abstract The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources and powerful computational cloud is tapped for enabling intensive applications on SMDs. In Mobile Cloud Computing (MCC), application processing services of computational clouds are leveraged for alleviating resource limitations in SMDs. The particular deficiency of distributed architecture and runtime partitioning of the elastic mobile application are the challenging aspects of current offloading models. To address these issues of traditional models for computational offloading in MCC, this paper proposes a novel distributed and elastic applications processing (DEAP) model for intensive applications in MCC. We present an analytical model to evaluate the proposed DEAP model, and test a prototype application in the real MCC environment to demonstrate the usefulness of DEAP model. Computational offloading using the DEAP model minimizes resources utilization on SMD in the distributed processing of intensive mobile applications. Evaluation indicates a reduction of 74.6% in the overhead of runtime application partitioning and a 66.6% reduction in the CPU utilization for the execution of the application on SMD. Application processing model Mobile Cloud Computing Smart mobile devices Distributed application Gani, Abdullah aut Khokhar, Rashid aut Rahman, Azizur aut Iftikhar, Mohsin (orcid)0000-0003-4736-0671 aut Chilamkurti, Naveen aut Enthalten in Wireless personal communications Springer US, 1994 95(2017), 4 vom: 13. März, Seite 4403-4423 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:95 year:2017 number:4 day:13 month:03 pages:4403-4423 https://doi.org/10.1007/s11277-017-4086-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 GBV_ILN_4266 AR 95 2017 4 13 03 4403-4423 |
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A Distributed and Elastic Application Processing Model for Mobile Cloud Computing |
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Abstract The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources and powerful computational cloud is tapped for enabling intensive applications on SMDs. In Mobile Cloud Computing (MCC), application processing services of computational clouds are leveraged for alleviating resource limitations in SMDs. The particular deficiency of distributed architecture and runtime partitioning of the elastic mobile application are the challenging aspects of current offloading models. To address these issues of traditional models for computational offloading in MCC, this paper proposes a novel distributed and elastic applications processing (DEAP) model for intensive applications in MCC. We present an analytical model to evaluate the proposed DEAP model, and test a prototype application in the real MCC environment to demonstrate the usefulness of DEAP model. Computational offloading using the DEAP model minimizes resources utilization on SMD in the distributed processing of intensive mobile applications. Evaluation indicates a reduction of 74.6% in the overhead of runtime application partitioning and a 66.6% reduction in the CPU utilization for the execution of the application on SMD. © Springer Science+Business Media New York 2017 |
| abstractGer |
Abstract The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources and powerful computational cloud is tapped for enabling intensive applications on SMDs. In Mobile Cloud Computing (MCC), application processing services of computational clouds are leveraged for alleviating resource limitations in SMDs. The particular deficiency of distributed architecture and runtime partitioning of the elastic mobile application are the challenging aspects of current offloading models. To address these issues of traditional models for computational offloading in MCC, this paper proposes a novel distributed and elastic applications processing (DEAP) model for intensive applications in MCC. We present an analytical model to evaluate the proposed DEAP model, and test a prototype application in the real MCC environment to demonstrate the usefulness of DEAP model. Computational offloading using the DEAP model minimizes resources utilization on SMD in the distributed processing of intensive mobile applications. Evaluation indicates a reduction of 74.6% in the overhead of runtime application partitioning and a 66.6% reduction in the CPU utilization for the execution of the application on SMD. © Springer Science+Business Media New York 2017 |
| abstract_unstemmed |
Abstract The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources and powerful computational cloud is tapped for enabling intensive applications on SMDs. In Mobile Cloud Computing (MCC), application processing services of computational clouds are leveraged for alleviating resource limitations in SMDs. The particular deficiency of distributed architecture and runtime partitioning of the elastic mobile application are the challenging aspects of current offloading models. To address these issues of traditional models for computational offloading in MCC, this paper proposes a novel distributed and elastic applications processing (DEAP) model for intensive applications in MCC. We present an analytical model to evaluate the proposed DEAP model, and test a prototype application in the real MCC environment to demonstrate the usefulness of DEAP model. Computational offloading using the DEAP model minimizes resources utilization on SMD in the distributed processing of intensive mobile applications. Evaluation indicates a reduction of 74.6% in the overhead of runtime application partitioning and a 66.6% reduction in the CPU utilization for the execution of the application on SMD. © Springer Science+Business Media New York 2017 |
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| title_short |
A Distributed and Elastic Application Processing Model for Mobile Cloud Computing |
| url |
https://doi.org/10.1007/s11277-017-4086-6 |
| remote_bool |
false |
| author2 |
Gani, Abdullah Khokhar, Rashid Rahman, Azizur Iftikhar, Mohsin Chilamkurti, Naveen |
| author2Str |
Gani, Abdullah Khokhar, Rashid Rahman, Azizur Iftikhar, Mohsin Chilamkurti, Naveen |
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188950273 |
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| doi_str |
10.1007/s11277-017-4086-6 |
| up_date |
2024-07-03T20:42:52.889Z |
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7.3994265 |