Mobility-Aware Task Offloading and Migration Schemes in Fog Computing Networks

Fog computing is an extension of cloud computing, which emphasizes distributed computing and provides computing service closer to user equipments (UEs). However, due to the limited service coverage of fog computing nodes (FCNs), the moving users may be out of the coverage, which would cause the radi...
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Autor*in:	Dongyu Wang [verfasserIn] Zhaolin Liu [verfasserIn] Xiaoxiang Wang [verfasserIn] Yanwen Lan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Fog computing mobility Gini coefficient resource optimization utility function

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 43356-43368
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:43356-43368

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2908263

Katalog-ID:	DOAJ071421718

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allfields	10.1109/ACCESS.2019.2908263 doi (DE-627)DOAJ071421718 (DE-599)DOAJedb9772251c34560b9a7361779b98235 DE-627 ger DE-627 rakwb eng TK1-9971 Dongyu Wang verfasserin aut Mobility-Aware Task Offloading and Migration Schemes in Fog Computing Networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fog computing is an extension of cloud computing, which emphasizes distributed computing and provides computing service closer to user equipments (UEs). However, due to the limited service coverage of fog computing nodes (FCNs), the moving users may be out of the coverage, which would cause the radio handover and execution results migration when the tasks are off-loaded to FCNs. Furthermore, extra cost, including energy consumption and latency, is generated and affects the revenue of UEs. Previous works rarely consider the mobility of UEs in fog computing networks. In this paper, a generic three-layer fog computing networks architecture is considered, and the mobility of UEs is characterized by the sojourn time in each coverage of FCNs, which follows the exponential distribution. To maximize the revenue of UEs, the off-loading decisions and computation resource allocation are jointly optimized to reduce the probability of migration. The problem is modeled as a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. The problem is divided into two parts: tasks off-loading and resource allocation. A Gini coefficient-based FCNs selection algorithm (GCFSA) is proposed to get a sub-optimal off-loading strategy, and a distributed resource optimization algorithm based on genetic algorithm (ROAGA) is implemented to solve the computation resource allocation problem. The proposed algorithms can handle the scenario of UEs' mobility in fog computing networks by significantly reducing the probability of migration. Simulations demonstrate that the proposed algorithms can achieve quasi-optimal revenue performance compared with other baseline algorithms. Fog computing mobility Gini coefficient resource optimization utility function Electrical engineering. Electronics. Nuclear engineering Zhaolin Liu verfasserin aut Xiaoxiang Wang verfasserin aut Yanwen Lan verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 43356-43368 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:43356-43368 https://doi.org/10.1109/ACCESS.2019.2908263 kostenfrei https://doaj.org/article/edb9772251c34560b9a7361779b98235 kostenfrei https://ieeexplore.ieee.org/document/8676263/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 43356-43368
spelling	10.1109/ACCESS.2019.2908263 doi (DE-627)DOAJ071421718 (DE-599)DOAJedb9772251c34560b9a7361779b98235 DE-627 ger DE-627 rakwb eng TK1-9971 Dongyu Wang verfasserin aut Mobility-Aware Task Offloading and Migration Schemes in Fog Computing Networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fog computing is an extension of cloud computing, which emphasizes distributed computing and provides computing service closer to user equipments (UEs). However, due to the limited service coverage of fog computing nodes (FCNs), the moving users may be out of the coverage, which would cause the radio handover and execution results migration when the tasks are off-loaded to FCNs. Furthermore, extra cost, including energy consumption and latency, is generated and affects the revenue of UEs. Previous works rarely consider the mobility of UEs in fog computing networks. In this paper, a generic three-layer fog computing networks architecture is considered, and the mobility of UEs is characterized by the sojourn time in each coverage of FCNs, which follows the exponential distribution. To maximize the revenue of UEs, the off-loading decisions and computation resource allocation are jointly optimized to reduce the probability of migration. The problem is modeled as a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. The problem is divided into two parts: tasks off-loading and resource allocation. A Gini coefficient-based FCNs selection algorithm (GCFSA) is proposed to get a sub-optimal off-loading strategy, and a distributed resource optimization algorithm based on genetic algorithm (ROAGA) is implemented to solve the computation resource allocation problem. The proposed algorithms can handle the scenario of UEs' mobility in fog computing networks by significantly reducing the probability of migration. Simulations demonstrate that the proposed algorithms can achieve quasi-optimal revenue performance compared with other baseline algorithms. Fog computing mobility Gini coefficient resource optimization utility function Electrical engineering. Electronics. Nuclear engineering Zhaolin Liu verfasserin aut Xiaoxiang Wang verfasserin aut Yanwen Lan verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 43356-43368 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:43356-43368 https://doi.org/10.1109/ACCESS.2019.2908263 kostenfrei https://doaj.org/article/edb9772251c34560b9a7361779b98235 kostenfrei https://ieeexplore.ieee.org/document/8676263/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 43356-43368
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callnumber-first	T - Technology
author	Dongyu Wang
spellingShingle	Dongyu Wang misc TK1-9971 misc Fog computing misc mobility misc Gini coefficient misc resource optimization misc utility function misc Electrical engineering. Electronics. Nuclear engineering Mobility-Aware Task Offloading and Migration Schemes in Fog Computing Networks
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topic_title	TK1-9971 Mobility-Aware Task Offloading and Migration Schemes in Fog Computing Networks Fog computing mobility Gini coefficient resource optimization utility function
topic	misc TK1-9971 misc Fog computing misc mobility misc Gini coefficient misc resource optimization misc utility function misc Electrical engineering. Electronics. Nuclear engineering
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title	Mobility-Aware Task Offloading and Migration Schemes in Fog Computing Networks
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title_sort	mobility-aware task offloading and migration schemes in fog computing networks
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title_auth	Mobility-Aware Task Offloading and Migration Schemes in Fog Computing Networks
abstract	Fog computing is an extension of cloud computing, which emphasizes distributed computing and provides computing service closer to user equipments (UEs). However, due to the limited service coverage of fog computing nodes (FCNs), the moving users may be out of the coverage, which would cause the radio handover and execution results migration when the tasks are off-loaded to FCNs. Furthermore, extra cost, including energy consumption and latency, is generated and affects the revenue of UEs. Previous works rarely consider the mobility of UEs in fog computing networks. In this paper, a generic three-layer fog computing networks architecture is considered, and the mobility of UEs is characterized by the sojourn time in each coverage of FCNs, which follows the exponential distribution. To maximize the revenue of UEs, the off-loading decisions and computation resource allocation are jointly optimized to reduce the probability of migration. The problem is modeled as a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. The problem is divided into two parts: tasks off-loading and resource allocation. A Gini coefficient-based FCNs selection algorithm (GCFSA) is proposed to get a sub-optimal off-loading strategy, and a distributed resource optimization algorithm based on genetic algorithm (ROAGA) is implemented to solve the computation resource allocation problem. The proposed algorithms can handle the scenario of UEs' mobility in fog computing networks by significantly reducing the probability of migration. Simulations demonstrate that the proposed algorithms can achieve quasi-optimal revenue performance compared with other baseline algorithms.
abstractGer	Fog computing is an extension of cloud computing, which emphasizes distributed computing and provides computing service closer to user equipments (UEs). However, due to the limited service coverage of fog computing nodes (FCNs), the moving users may be out of the coverage, which would cause the radio handover and execution results migration when the tasks are off-loaded to FCNs. Furthermore, extra cost, including energy consumption and latency, is generated and affects the revenue of UEs. Previous works rarely consider the mobility of UEs in fog computing networks. In this paper, a generic three-layer fog computing networks architecture is considered, and the mobility of UEs is characterized by the sojourn time in each coverage of FCNs, which follows the exponential distribution. To maximize the revenue of UEs, the off-loading decisions and computation resource allocation are jointly optimized to reduce the probability of migration. The problem is modeled as a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. The problem is divided into two parts: tasks off-loading and resource allocation. A Gini coefficient-based FCNs selection algorithm (GCFSA) is proposed to get a sub-optimal off-loading strategy, and a distributed resource optimization algorithm based on genetic algorithm (ROAGA) is implemented to solve the computation resource allocation problem. The proposed algorithms can handle the scenario of UEs' mobility in fog computing networks by significantly reducing the probability of migration. Simulations demonstrate that the proposed algorithms can achieve quasi-optimal revenue performance compared with other baseline algorithms.
abstract_unstemmed	Fog computing is an extension of cloud computing, which emphasizes distributed computing and provides computing service closer to user equipments (UEs). However, due to the limited service coverage of fog computing nodes (FCNs), the moving users may be out of the coverage, which would cause the radio handover and execution results migration when the tasks are off-loaded to FCNs. Furthermore, extra cost, including energy consumption and latency, is generated and affects the revenue of UEs. Previous works rarely consider the mobility of UEs in fog computing networks. In this paper, a generic three-layer fog computing networks architecture is considered, and the mobility of UEs is characterized by the sojourn time in each coverage of FCNs, which follows the exponential distribution. To maximize the revenue of UEs, the off-loading decisions and computation resource allocation are jointly optimized to reduce the probability of migration. The problem is modeled as a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. The problem is divided into two parts: tasks off-loading and resource allocation. A Gini coefficient-based FCNs selection algorithm (GCFSA) is proposed to get a sub-optimal off-loading strategy, and a distributed resource optimization algorithm based on genetic algorithm (ROAGA) is implemented to solve the computation resource allocation problem. The proposed algorithms can handle the scenario of UEs' mobility in fog computing networks by significantly reducing the probability of migration. Simulations demonstrate that the proposed algorithms can achieve quasi-optimal revenue performance compared with other baseline algorithms.
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title_short	Mobility-Aware Task Offloading and Migration Schemes in Fog Computing Networks
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