Cost optimization in edge computing: a survey

Abstract The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage,...
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Autor*in:	Cao, Liming [verfasserIn] Huo, Tao [verfasserIn] Li, Shaobo [verfasserIn] Zhang, Xingxing [verfasserIn] Chen, Yanchi [verfasserIn] Lin, Guangzheng [verfasserIn] Wu, Fengbin [verfasserIn] Ling, Yihong [verfasserIn] Zhou, Yaxin [verfasserIn] Xie, Qun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Artificial intelligence Computation offloading Edge computing Internet of things Resource scheduling

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Artificial intelligence review - Springer Netherlands, 1986, 57(2024), 11 vom: 01. Okt.
Übergeordnetes Werk:	volume:57 ; year:2024 ; number:11 ; day:01 ; month:10

Links:	Volltext

DOI / URN:	10.1007/s10462-024-10947-4

Katalog-ID:	SPR057618437

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allfields	10.1007/s10462-024-10947-4 doi (DE-627)SPR057618437 (SPR)s10462-024-10947-4-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 77.31 bkl Cao, Liming verfasserin aut Cost optimization in edge computing: a survey 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions. Artificial intelligence (dpeaa)DE-He213 Computation offloading (dpeaa)DE-He213 Edge computing (dpeaa)DE-He213 Internet of things (dpeaa)DE-He213 Resource scheduling (dpeaa)DE-He213 Huo, Tao verfasserin aut Li, Shaobo verfasserin aut Zhang, Xingxing verfasserin aut Chen, Yanchi verfasserin aut Lin, Guangzheng verfasserin aut Wu, Fengbin verfasserin aut Ling, Yihong verfasserin aut Zhou, Yaxin verfasserin aut Xie, Qun verfasserin aut Enthalten in Artificial intelligence review Springer Netherlands, 1986 57(2024), 11 vom: 01. Okt. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:11 day:01 month:10 https://dx.doi.org/10.1007/s10462-024-10947-4 X:SPRINGER Resolving-System kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.72 VZ 77.31 VZ AR 57 2024 11 01 10
spelling	10.1007/s10462-024-10947-4 doi (DE-627)SPR057618437 (SPR)s10462-024-10947-4-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 77.31 bkl Cao, Liming verfasserin aut Cost optimization in edge computing: a survey 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions. Artificial intelligence (dpeaa)DE-He213 Computation offloading (dpeaa)DE-He213 Edge computing (dpeaa)DE-He213 Internet of things (dpeaa)DE-He213 Resource scheduling (dpeaa)DE-He213 Huo, Tao verfasserin aut Li, Shaobo verfasserin aut Zhang, Xingxing verfasserin aut Chen, Yanchi verfasserin aut Lin, Guangzheng verfasserin aut Wu, Fengbin verfasserin aut Ling, Yihong verfasserin aut Zhou, Yaxin verfasserin aut Xie, Qun verfasserin aut Enthalten in Artificial intelligence review Springer Netherlands, 1986 57(2024), 11 vom: 01. Okt. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:11 day:01 month:10 https://dx.doi.org/10.1007/s10462-024-10947-4 X:SPRINGER Resolving-System kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.72 VZ 77.31 VZ AR 57 2024 11 01 10
allfields_unstemmed	10.1007/s10462-024-10947-4 doi (DE-627)SPR057618437 (SPR)s10462-024-10947-4-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 77.31 bkl Cao, Liming verfasserin aut Cost optimization in edge computing: a survey 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions. Artificial intelligence (dpeaa)DE-He213 Computation offloading (dpeaa)DE-He213 Edge computing (dpeaa)DE-He213 Internet of things (dpeaa)DE-He213 Resource scheduling (dpeaa)DE-He213 Huo, Tao verfasserin aut Li, Shaobo verfasserin aut Zhang, Xingxing verfasserin aut Chen, Yanchi verfasserin aut Lin, Guangzheng verfasserin aut Wu, Fengbin verfasserin aut Ling, Yihong verfasserin aut Zhou, Yaxin verfasserin aut Xie, Qun verfasserin aut Enthalten in Artificial intelligence review Springer Netherlands, 1986 57(2024), 11 vom: 01. Okt. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:11 day:01 month:10 https://dx.doi.org/10.1007/s10462-024-10947-4 X:SPRINGER Resolving-System kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.72 VZ 77.31 VZ AR 57 2024 11 01 10
allfieldsGer	10.1007/s10462-024-10947-4 doi (DE-627)SPR057618437 (SPR)s10462-024-10947-4-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 77.31 bkl Cao, Liming verfasserin aut Cost optimization in edge computing: a survey 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions. Artificial intelligence (dpeaa)DE-He213 Computation offloading (dpeaa)DE-He213 Edge computing (dpeaa)DE-He213 Internet of things (dpeaa)DE-He213 Resource scheduling (dpeaa)DE-He213 Huo, Tao verfasserin aut Li, Shaobo verfasserin aut Zhang, Xingxing verfasserin aut Chen, Yanchi verfasserin aut Lin, Guangzheng verfasserin aut Wu, Fengbin verfasserin aut Ling, Yihong verfasserin aut Zhou, Yaxin verfasserin aut Xie, Qun verfasserin aut Enthalten in Artificial intelligence review Springer Netherlands, 1986 57(2024), 11 vom: 01. Okt. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:11 day:01 month:10 https://dx.doi.org/10.1007/s10462-024-10947-4 X:SPRINGER Resolving-System kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.72 VZ 77.31 VZ AR 57 2024 11 01 10
allfieldsSound	10.1007/s10462-024-10947-4 doi (DE-627)SPR057618437 (SPR)s10462-024-10947-4-e DE-627 ger DE-627 rakwb eng 004 VZ 54.72 bkl 77.31 bkl Cao, Liming verfasserin aut Cost optimization in edge computing: a survey 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions. Artificial intelligence (dpeaa)DE-He213 Computation offloading (dpeaa)DE-He213 Edge computing (dpeaa)DE-He213 Internet of things (dpeaa)DE-He213 Resource scheduling (dpeaa)DE-He213 Huo, Tao verfasserin aut Li, Shaobo verfasserin aut Zhang, Xingxing verfasserin aut Chen, Yanchi verfasserin aut Lin, Guangzheng verfasserin aut Wu, Fengbin verfasserin aut Ling, Yihong verfasserin aut Zhou, Yaxin verfasserin aut Xie, Qun verfasserin aut Enthalten in Artificial intelligence review Springer Netherlands, 1986 57(2024), 11 vom: 01. Okt. (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:57 year:2024 number:11 day:01 month:10 https://dx.doi.org/10.1007/s10462-024-10947-4 X:SPRINGER Resolving-System kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_187 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 54.72 VZ 77.31 VZ AR 57 2024 11 01 10
language	English
source	Enthalten in Artificial intelligence review 57(2024), 11 vom: 01. Okt. volume:57 year:2024 number:11 day:01 month:10
sourceStr	Enthalten in Artificial intelligence review 57(2024), 11 vom: 01. Okt. volume:57 year:2024 number:11 day:01 month:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Artificial intelligence Computation offloading Edge computing Internet of things Resource scheduling
dewey-raw	004
isfreeaccess_bool	true
container_title	Artificial intelligence review
authorswithroles_txt_mv	Cao, Liming @@aut@@ Huo, Tao @@aut@@ Li, Shaobo @@aut@@ Zhang, Xingxing @@aut@@ Chen, Yanchi @@aut@@ Lin, Guangzheng @@aut@@ Wu, Fengbin @@aut@@ Ling, Yihong @@aut@@ Zhou, Yaxin @@aut@@ Xie, Qun @@aut@@
publishDateDaySort_date	2024-10-01T00:00:00Z
hierarchy_top_id	27134945X
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author	Cao, Liming
spellingShingle	Cao, Liming ddc 004 bkl 54.72 bkl 77.31 misc Artificial intelligence misc Computation offloading misc Edge computing misc Internet of things misc Resource scheduling Cost optimization in edge computing: a survey
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title	Cost optimization in edge computing: a survey
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title_sort	cost optimization in edge computing: a survey
title_auth	Cost optimization in edge computing: a survey
abstract	Abstract The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions. © The Author(s) 2024
abstractGer	Abstract The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions. © The Author(s) 2024
abstract_unstemmed	Abstract The edge computing paradigm is becoming increasingly commercialized due to the widespread adoption of wireless communication technologies and the growing demand for compute-intensive mobile applications. Edge computing complements the cloud computing model by deploying computation, storage, and network resources to the edge locations of wireless access networks, empowering end devices to run resource-intensive applications. In order to promote the commercialization of edge computing, it is important to explore effective ways to reduce the cost of edge computing networks. This paper provides a comprehensive review of the research findings in recent years, offering a clear perspective on the research dynamics. This paper first recalls the architectural framework of edge computing. Then, the main optimization objectives and optimization methods are comprehensively described. Mainstream mathematical models for cost reduction are then shown in depth. The paper also discusses the methods used to evaluate the effectiveness. Then, typical examples of typical application scenarios for edge computing networks are examined in depth. Finally, the paper identifies some unresolved issues. We expect future research to make more attempts in these directions. © The Author(s) 2024
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title_short	Cost optimization in edge computing: a survey
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