A survey on computation offloading and service placement in fog computing-based IoT

In recent years, fog computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited Internet of Things (IoT) devices. The main feature of fog computing is to push computation, networking, and storage facilities closer to...

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Published in:The Journal of supercomputing Vol. 78; no. 2; pp. 1983 - 2014
Main Authors: Gasmi, Kaouther, Dilek, Selma, Tosun, Suleyman, Ozdemir, Suat
Format: Journal Article
Language:English
Published: New York Springer US 01.02.2022
Springer Nature B.V
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ISSN:0920-8542, 1573-0484
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Abstract In recent years, fog computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited Internet of Things (IoT) devices. The main feature of fog computing is to push computation, networking, and storage facilities closer to the network edge. This enables IoT user equipment (UE) to profit from the fog computing paradigm by mainly offloading their intensive computation tasks to fog resources. Thus, computation offloading and service placement mechanisms can overcome the resource constraints of IoT devices, and improve the system performance in terms of increasing battery lifetime of UE and reducing the total delay. In this paper, we survey the current research conducted on computation offloading and service placement in fog computing-based IoT in a comparative manner.
AbstractList In recent years, fog computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited Internet of Things (IoT) devices. The main feature of fog computing is to push computation, networking, and storage facilities closer to the network edge. This enables IoT user equipment (UE) to profit from the fog computing paradigm by mainly offloading their intensive computation tasks to fog resources. Thus, computation offloading and service placement mechanisms can overcome the resource constraints of IoT devices, and improve the system performance in terms of increasing battery lifetime of UE and reducing the total delay. In this paper, we survey the current research conducted on computation offloading and service placement in fog computing-based IoT in a comparative manner.
Author Ozdemir, Suat
Dilek, Selma
Tosun, Suleyman
Gasmi, Kaouther
Author_xml – sequence: 1
  givenname: Kaouther
  surname: Gasmi
  fullname: Gasmi, Kaouther
  organization: Higher Institute of Applied Languages and Computer Sciences in Beja
– sequence: 2
  givenname: Selma
  surname: Dilek
  fullname: Dilek, Selma
  organization: Graduate School of Science and Engineering, Hacettepe University
– sequence: 3
  givenname: Suleyman
  orcidid: 0000-0002-3708-2009
  surname: Tosun
  fullname: Tosun, Suleyman
  email: stosun@cs.hacettepe.edu.tr
  organization: Department of Computer Engineering, Hacettepe University
– sequence: 4
  givenname: Suat
  surname: Ozdemir
  fullname: Ozdemir, Suat
  organization: Department of Computer Engineering, Hacettepe University
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Cites_doi 10.1155/2018/1386470
10.1109/JIOT.2018.2889511
10.3390/s18082423
10.1109/MPRV.2009.82
10.1080/17517575.2017.1304579
10.23919/INM.2017.7987464
10.1016/j.iot.2019.100070
10.1109/COMST.2017.2771153
10.1016/j.sysarc.2019.02.009
10.1007/978-3-319-60717-7-29
10.1109/TCOMM.2017.2787700
10.1145/2811587.2811598
10.1145/1721654.1721672
10.1109/GLOCOM.2017.8254207
10.1016/j.iot.2020.100177
10.1109/ICDCS.2017.226
10.1145/3362031
10.1109/COMST.2017.2745201
10.1007/s11761-017-0219-8
10.1145/3391196
10.1145/3403955
10.1145/3234463
10.1109/MWC.2019.1700441
10.1016/j.dcan.2018.10.008
10.1016/j.compeleceng.2018.02.047
10.1109/JSAC.2016.2611964
10.1109/ACCESS.2018.2866491
10.1016/j.future.2020.12.021
10.1145/2342509.2342513
10.1109/ICFEC.2017.20
10.1016/j.future.2018.04.057
10.1002/cpe.5581
10.1109/LCOMM.2017.2690678
10.1109/SOSE.2013.73
10.1007/s10723-015-9356-5
10.1109/CloudCom.2017.45
10.1109/CIT/IUCC/DASC/PICOM.2015.51
10.3390/s19092122
10.1109/TC.2016.2536019
10.1016/j.jnca.2017.09.002
10.1371/journal.pone.0224934
10.1145/3287921.3287984
10.1145/3326066
10.1016/j.comnet.2020.107496
10.1109/COMST.2017.2682318
10.1007/978-981-10-5861-5_5
10.1007/s12243-016-0524-9
10.1007/s10723-019-09491-1
10.1109/JIOT.2018.2838022
10.1109/IEEE.EDGE.2017.12
10.1109/COMST.2018.2814571
10.1109/ACSSC.2015.7421170
10.1109/NAS.2015.7255196
10.1007/978-3-319-57639-8-2
10.1016/j.pmcj.2018.12.007
10.1109/TVT.2018.2871414
10.1109/TNET.2018.2841758
10.1109/SOCA.2016.10
10.1145/3186592
10.1109/TCC.2016.2560808
10.1109/TII.2018.2842821
10.1109/TSC.2018.2827070
10.1145/3167132.3167215
10.1145/3093336.3037698
10.1109/HotWeb.2015.22
10.1109/NCA.2017.8171359
10.1109/ACCESS.2019.2946683
10.1007/978-3-319-94890-4_1
10.1109/JIOT.2017.2780236
10.1145/2757384.2757397
10.1109/GLOCOM.2018.8647488
10.1109/ICNN.1995.488968
10.1016/B978-0-12-805395-9.00004-6
10.1109/IE.2014.54
10.7551/mitpress/1090.001.0001
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Copyright The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
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Issue 2
Keywords Fog computing
Optimization algorithms
Internet of Things (IoT)
Computation offloading
Service placement
Language English
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References MahmoodZRamachandranMMahmoodZFog computing: concepts, principles and related paradigmsFog computing: concepts, frameworks and technologies, chap. 12018BerlinSpringer32110.1007/978-3-319-94890-4_1
Ma X, Lin C, Xiang X, Chen C (2015) Game-theoretic analysis of computation offloading for cloudlet-based mobile cloud computing. In: Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp 271–278
Gia TN, Jiang M, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H (2015) Fog computing in healthcare internet of things: a case study on ECG feature extraction. In: 2015 IEEE International Conference on Computer and Information Technology. IEEE, pp 356–363
Sundar S (2019) Optimization algorithms for task offloading and scheduling in cloud computing. Ph.D. thesis
PhanLANguyenDTLeeMParkDHKimTDynamic fog-to-fog offloading in SDN-based fog computing systemsFutur Gener Comput Syst202111748649710.1016/j.future.2020.12.021
LiLOtaKDongMDeep learning for smart industry: efficient manufacture inspection system with fog computingIEEE Trans Ind Inf201814104665467310.1109/TII.2018.2842821
LiuLChangZGuoXMaoSRistaniemiTMultiobjective optimization for computation offloading in fog computingIEEE Internet Things J20185128329410.1109/JIOT.2017.2780236
ShakaramiAGhobaei-AraniMShahidinejadAA survey on the computation offloading approaches in mobile edge computing: a machine learning-based perspectiveComput Netw202018210749610.1016/j.comnet.2020.107496
NahaRKGargSGeorgakopoulosDJayaramanPPGaoLXiangYRanjanRFog computing: survey of trends, architectures, requirements, and research directionsIEEE Access20186479804800910.1109/ACCESS.2018.2866491
SunXAnsariNLatency aware workload offloading in the cloudlet networkIEEE Commun Lett20172171481148410.1109/LCOMM.2017.2690678
SalahtFADesprezFLebreAAn overview of service placement problem in fog and edge computingACM Comput Surv202053313510.1145/3391196
WangYShengMWangXWangLLiJMobile-edge computing: partial computation offloading using dynamic voltage scalingIEEE Trans Commun2016641042684282
HuPDhelimSNingHQiuTSurvey on fog computing: architecture, key technologies, applications and open issuesJ Netw Comput Appl201710.1016/j.jnca.2017.09.002
Hardesty L (2017) Fog computing group publishes reference architecture. https://www.sdxcentral.com/articles/news/Fog-computing-group-publishes-reference-architecture/2017/02/. Accessed 20 April 2020
Dastjerdi AV, Gupta H, Calheiros RN, Ghosh SK, Buyya R (2016) Fog computing: principles, architectures, and applications. In: Internet of things. Elsevier, pp 61–75
Zhao X, Zhao L, Liang K (2017) An energy consumption oriented offloading algorithm for fog computing. In: Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, pp 293–301. Springer. https://doi.org/10.1007/978-3-319-60717-7-29
PaulAPinjariHHongWHSeoHCRhoSFog computing-based IoT for health monitoring systemJ Sens201810.1155/2018/1386470
MahmudRRamamohanaraoKBuyyaRLatency-aware application module management for fog computing environmentsACM Trans Internet Technol201819112110.1145/3186592
RenJZhangDHeSZhangYLiTA survey on end-edge-cloud orchestrated network computing paradigms: transparent computing, mobile edge computing, fog computing, and cloudletACM Comput Surv201910.1145/3362031
BitamSZeadallySMelloukAFog computing job scheduling optimization based on bees swarmEnterprise Inf Syst201812437339710.1080/17517575.2017.1304579
Li G, Liu Y, Wu J, Lin D, Zhao S (2019) Methods of resource scheduling based on optimized fuzzy clustering in fog computing. Sensors (Basel, Switzerland) 19(9). https://doi.org/10.3390/s19092122. https://europepmc.org/articles/PMC6539192
DuJZhaoLFengJChuXComputation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guaranteeIEEE Trans Commun20186641594160810.1109/TCOMM.2017.2787700
Hong H, Tsai P, Cheng A, Uddin MYS, Venkatasubramanian N, Hsu C (2017) Supporting internet-of-things analytics in a fog computing platform. In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp 138–145. https://doi.org/10.1109/CloudCom.2017.45
HongCHVargheseBResource management in fog/edge computing: a survey on architectures, infrastructure, and algorithmsACM Comput Surv201910.1145/3326066
MaoYZhangJLetaiefKBDynamic computation offloading for mobile-edge computing with energy harvesting devicesIEEE J Sel Areas Commun201634123590360510.1109/JSAC.2016.2611964
KangYHauswaldJGaoCRovinskiAMudgeTMarsJTangLNeurosurgeon: collaborative intelligence between the cloud and mobile edgeSIGPLAN Not.201752461562910.1145/3093336.3037698
TangZZhouXZhangFJiaWZhaoWMigration modeling and learning algorithms for containers in fog computingIEEE Trans Serv Comput201812571272510.1109/TSC.2018.2827070
Cao Yu, Chen Songqing, Hou Peng, Brown D (2015) Fast: a fog computing assisted distributed analytics system to monitor fall for stroke mitigation. In: 2015 IEEE International Conference on Networking, Architecture and Storage (NAS), pp 2–11. https://doi.org/10.1109/NAS.2015.7255196
SatyanarayananMBahlPCaceresRDaviesNThe case for VM-based cloudlets in mobile computingIEEE Pervasive Comput200984142310.1109/MPRV.2009.82
Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on mobile cloud computing, pp 13–16
La QD, Ngo MV, Dinh TQ, Quek TQ, Shin H (2019) Enabling intelligence in fog computing to achieve energy and latency reduction. Digital Commun Netw 5(1):3–9. https://doi.org/10.1016/j.dcan.2018.10.008, http://www.sciencedirect.com/science/article/pii/S2352864818301081. Artificial intelligence for future wireless communications and networking
Shah-MansouriHWongVWHierarchical fog-cloud computing for IoT systems: a computation offloading gameIEEE Internet Things J2018543246325710.1109/JIOT.2018.2838022
Taneja M, Davy A (2017) Resource aware placement of IoT application modules in fog-cloud computing paradigm. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). IEEE, pp 1222–1228
Jamil B, Shojafar M, Ahmed I, Ullah A, Munir K, Ijaz H (2020) A job scheduling algorithm for delay and performance optimization in fog computing. Concurr Comput Pract Exp 32(7). https://doi.org/10.1002/cpe.5581. https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5581. E5581 cpe.5581
WangJWuWLiaoZSangaiahAKSimon SherrattRAn energy-efficient off-loading scheme for low latency in collaborative edge computingIEEE Access2019714918214919010.1109/ACCESS.2019.2946683
Daneshfar N, Pappas N, Polishchuk V, Angelakis V (2018) Service allocation in a mobile fog infrastructure under availability and QOS constraints. In: 2018 IEEE Global Communications Conference (GLOBECOM). IEEE, pp 1–6
AazamMZeadallySHarrasKAOffloading in fog computing for IoT: review, enabling technologies, and research opportunitiesFuture Gener Comput Syst20188727828910.1016/j.future.2018.04.057
ArmbrustMFoxAGriffithRJosephADKatzRKonwinskiALeeGPattersonDRabkinAStoicaIA view of cloud computingCommun ACM2010534505810.1145/1721654.1721672
BadriHStochastic optimization methods for resource management in edge computing systems2019DetroitWayne State University
Nath SB, Gupta H, Chakraborty S, Ghosh SK (2018) A survey of fog computing and communication: current researches and future directions. arXiv preprint arXiv:1804.04365
Mahmud R, Kotagiri R, Buyya R (2018) Fog computing: a taxonomy, survey and future directions. In: Internet of everything. Springer, pp 103–130
YaseenSGAl-SlamyNAnt colony optimizationIJCSNS200886351
ChenLZhouSXuJComputation peer offloading for energy-constrained mobile edge computing in small-cell networksIEEE/ACM Trans Netw20182641619163210.1109/TNET.2018.2841758
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks. IEEE, vol 4, pp 1942–1948
MahmoodiSEUmaRNSubbalakshmiKPOptimal joint scheduling and cloud offloading for mobile applicationsIEEE Trans Cloud Comput20167230131310.1109/TCC.2016.2560808
ZengDGuLGuoSChengZYuSJoint optimization of task scheduling and image placement in fog computing supported software-defined embedded systemIEEE Trans Comput2016651237023712357076910.1109/TC.2016.25360191360.68298
GuBChenYLiaoHZhouZZhangDA distributed and context-aware task assignment mechanism for collaborative mobile edge computingSensors2018188242310.3390/s18082423
Wright KL (2019) High-performance distributed computing techniques for wireless IoT and connected vehicle systems. Ph.D. thesis, University of Southern California
NishaPFog computing and its real time applicationsInt J Emerg Technol Adv Eng201556266269
HollandJHAdaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence1992CambridgeMIT Press10.7551/mitpress/1090.001.0001
MaoYYouCZhangJHuangKLetaiefKBA survey on mobile edge computing: the communication perspectiveIEEE Commun Surv Tutor20171942322235810.1109/COMST.2017.2745201
Ghobaei-AraniMSouriARahmanianAResource management approaches in fog computing: a comprehensive reviewJ Grid Comput20191814210.1007/s10723-019-09491-1
Yi S, Hao Z, Qin Z, Li Q (2015) Fog computing: platform and applications. In: 2015 third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb). IEEE, pp 73–78
AazamMSt-HilaireMLungCHLambadarisIHuhENIoT resource estimation challenges and modeling in Fog2018ChamSpringer173110.1007/978-3-319-57639-8-2
MukherjeeMShuLWangDSurvey of fog computing: fundamental, network applications, and research challengesIEEE Commun Surv Tutor20182031826185710.1109/COMST.2018.2814571
LeraIGuerreroCJuizCAvailability-aware service placement policy in fog computing based on graph partitionsIEEE Internet Things J2018623641365110.1109/JIOT.2018.2889511
AlliAAAlamMMSecoff-fciot: machine learning based secure offloading in fog-cloud of things for smart city app
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References_xml – reference: MaoYZhangJLetaiefKBDynamic computation offloading for mobile-edge computing with energy harvesting devicesIEEE J Sel Areas Commun201634123590360510.1109/JSAC.2016.2611964
– reference: MahmudRRamamohanaraoKBuyyaRLatency-aware application module management for fog computing environmentsACM Trans Internet Technol201819112110.1145/3186592
– reference: Teerapittayanon S, McDanel B, Kung HT (2017) Distributed deep neural networks over the cloud, the edge and end devices. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp 328–339. https://doi.org/10.1109/ICDCS.2017.226
– reference: WangJWuWLiaoZSangaiahAKSimon SherrattRAn energy-efficient off-loading scheme for low latency in collaborative edge computingIEEE Access2019714918214919010.1109/ACCESS.2019.2946683
– reference: ArmbrustMFoxAGriffithRJosephADKatzRKonwinskiALeeGPattersonDRabkinAStoicaIA view of cloud computingCommun ACM2010534505810.1145/1721654.1721672
– reference: Skarlat O, Schulte S, Borkowski M, Leitner P (2016) Resource provisioning for IoT services in the fog. In: 2016 IEEE 9th International Conference on Service-oriented Computing and Applications (SOCA), pp 32–39 . https://doi.org/10.1109/SOCA.2016.10
– reference: Zhu J, Chan DS, Prabhu MS, Natarajan P, Hu H, Bonomi F (2013) Improving web sites performance using edge servers in fog computing architecture. In: 2013 IEEE Seventh International Symposium on Service-oriented System Engineering, pp 320–323 . https://doi.org/10.1109/SOSE.2013.73
– reference: VelasquezKAbreuDPCuradoMMonteiroEService placement for latency reduction in the internet of thingsAnn Telecommun2017721–210511510.1007/s12243-016-0524-9
– reference: MukherjeeMShuLWangDSurvey of fog computing: fundamental, network applications, and research challengesIEEE Commun Surv Tutor20182031826185710.1109/COMST.2018.2814571
– reference: SalahtFADesprezFLebreAAn overview of service placement problem in fog and edge computingACM Comput Surv202053313510.1145/3391196
– reference: KangYHauswaldJGaoCRovinskiAMudgeTMarsJTangLNeurosurgeon: collaborative intelligence between the cloud and mobile edgeSIGPLAN Not.201752461562910.1145/3093336.3037698
– reference: Ghobaei-AraniMSouriARahmanianAResource management approaches in fog computing: a comprehensive reviewJ Grid Comput20191814210.1007/s10723-019-09491-1
– reference: Hong H, Tsai P, Cheng A, Uddin MYS, Venkatasubramanian N, Hsu C (2017) Supporting internet-of-things analytics in a fog computing platform. In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp 138–145. https://doi.org/10.1109/CloudCom.2017.45
– reference: YaseenSGAl-SlamyNAnt colony optimizationIJCSNS200886351
– reference: Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on mobile cloud computing, pp 13–16
– reference: BellavistaPBerrocalJCorradiADasSKFoschiniLZanniAA survey on fog computing for the internet of thingsPervasive Mobile Comput201952719910.1016/j.pmcj.2018.12.007
– reference: Nath SB, Gupta H, Chakraborty S, Ghosh SK (2018) A survey of fog computing and communication: current researches and future directions. arXiv preprint arXiv:1804.04365
– reference: Yi S, Li C, Li Q (2015) A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data, pp 37–42
– reference: MahmudRRamamohanaraoKBuyyaRApplication management in fog computing environments: a taxonomy, review and future directionsACM Comput Surv202010.1145/3403955
– reference: Ma X, Lin C, Xiang X, Chen C (2015) Game-theoretic analysis of computation offloading for cloudlet-based mobile cloud computing. In: Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp 271–278
– reference: DingHFangYVirtual infrastructure at traffic lights: vehicular temporary storage assisted data transportation at signalized intersectionsIEEE Trans Veh Technol20186712124521245610.1109/TVT.2018.2871414
– reference: NahaRKGargSGeorgakopoulosDJayaramanPPGaoLXiangYRanjanRFog computing: survey of trends, architectures, requirements, and research directionsIEEE Access20186479804800910.1109/ACCESS.2018.2866491
– reference: ShuklaSHassanMFKhanMKJungLTAwangAAn analytical model to minimize the latency in healthcare internet-of-things in fog computing environmentPLoS ONE20191411e022493410.1371/journal.pone.0224934
– reference: Cao Yu, Chen Songqing, Hou Peng, Brown D (2015) Fast: a fog computing assisted distributed analytics system to monitor fall for stroke mitigation. In: 2015 IEEE International Conference on Networking, Architecture and Storage (NAS), pp 2–11. https://doi.org/10.1109/NAS.2015.7255196
– reference: IDC: Iot growth demands rethink of long-term storage strategies (2020). https://www.idc.com/getdoc.jsp?containerId=prAP46737220
– reference: HongCHVargheseBResource management in fog/edge computing: a survey on architectures, infrastructure, and algorithmsACM Comput Surv201910.1145/3326066
– reference: NingZHuangJWangXVehicular fog computing: enabling real-time traffic management for smart citiesIEEE Wirel Commun2019261879310.1109/MWC.2019.1700441
– reference: SatyanarayananMBahlPCaceresRDaviesNThe case for VM-based cloudlets in mobile computingIEEE Pervasive Comput200984142310.1109/MPRV.2009.82
– reference: LeraIGuerreroCJuizCAvailability-aware service placement policy in fog computing based on graph partitionsIEEE Internet Things J2018623641365110.1109/JIOT.2018.2889511
– reference: Craciunescu R, Mihovska A, Mihaylov M, Kyriazakos S, Prasad R, Halunga S (2015) Implementation of fog computing for reliable e-health applications. In: 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, pp 459–463
– reference: Varshney P, Simmhan Y (2017) Demystifying fog computing: characterizing architectures, applications and abstractions. In: 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC). IEEE, pp 115–124
– reference: Hardesty L (2017) Fog computing group publishes reference architecture. https://www.sdxcentral.com/articles/news/Fog-computing-group-publishes-reference-architecture/2017/02/. Accessed 20 April 2020
– reference: La QD, Ngo MV, Dinh TQ, Quek TQ, Shin H (2019) Enabling intelligence in fog computing to achieve energy and latency reduction. Digital Commun Netw 5(1):3–9. https://doi.org/10.1016/j.dcan.2018.10.008, http://www.sciencedirect.com/science/article/pii/S2352864818301081. Artificial intelligence for future wireless communications and networking
– reference: RenJZhangDHeSZhangYLiTA survey on end-edge-cloud orchestrated network computing paradigms: transparent computing, mobile edge computing, fog computing, and cloudletACM Comput Surv201910.1145/3362031
– reference: ZengDGuLGuoSChengZYuSJoint optimization of task scheduling and image placement in fog computing supported software-defined embedded systemIEEE Trans Comput2016651237023712357076910.1109/TC.2016.25360191360.68298
– reference: AazamMHuhENSt-HilaireMTowards media inter-cloud standardization-evaluating impact of cloud storage heterogeneityJ Grid Comput201816342544310.1007/s10723-015-9356-5
– reference: Xia Y, Etchevers X, Letondeur L, Coupaye T, Desprez F (2018) Combining hardware nodes and software components ordering-based heuristics for optimizing the placement of distributed IoT applications in the fog. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC ’18, p 751–760. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3167132.3167215
– reference: LiHOtaKDongMDeep reinforcement scheduling for mobile crowdsensing in fog computingACM Trans Internet Technol201910.1145/3234463
– reference: HollandJHAdaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence1992CambridgeMIT Press10.7551/mitpress/1090.001.0001
– reference: BitamSZeadallySMelloukAFog computing job scheduling optimization based on bees swarmEnterprise Inf Syst201812437339710.1080/17517575.2017.1304579
– reference: Statista: Internet of things (IoT) connected devices installed base worldwide from 2015 to 2025 (2016). https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/
– reference: DanielASubburathinamKPaulARajkumarNRhoSBig autonomous vehicular data classifications: towards procuring intelligenceVeh Commun20179306312
– reference: AlliAAAlamMMSecoff-fciot: machine learning based secure offloading in fog-cloud of things for smart city applicationsInternet Things2019710007010.1016/j.iot.2019.100070
– reference: Sundar S (2019) Optimization algorithms for task offloading and scheduling in cloud computing. Ph.D. thesis
– reference: PaulAPinjariHHongWHSeoHCRhoSFog computing-based IoT for health monitoring systemJ Sens201810.1155/2018/1386470
– reference: Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks. IEEE, vol 4, pp 1942–1948
– reference: Taneja M, Davy A (2017) Resource aware placement of IoT application modules in fog-cloud computing paradigm. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). IEEE, pp 1222–1228
– reference: Jamil B, Shojafar M, Ahmed I, Ullah A, Munir K, Ijaz H (2020) A job scheduling algorithm for delay and performance optimization in fog computing. Concurr Comput Pract Exp 32(7). https://doi.org/10.1002/cpe.5581. https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5581. E5581 cpe.5581
– reference: DuJZhaoLFengJChuXComputation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guaranteeIEEE Trans Commun20186641594160810.1109/TCOMM.2017.2787700
– reference: Li G, Liu Y, Wu J, Lin D, Zhao S (2019) Methods of resource scheduling based on optimized fuzzy clustering in fog computing. Sensors (Basel, Switzerland) 19(9). https://doi.org/10.3390/s19092122. https://europepmc.org/articles/PMC6539192
– reference: HuPDhelimSNingHQiuTSurvey on fog computing: architecture, key technologies, applications and open issuesJ Netw Comput Appl201710.1016/j.jnca.2017.09.002
– reference: Filippo Poltronieri Mauro Tortonesi CS, Sur N (2021) Reinforcement learning for value-based placement of fog services
– reference: HuPDhelimSNingHQiuTSurvey on fog computing: architecture, key technologies, applications and open issuesJ Netw Comput Appl201798274210.1016/j.jnca.2017.09.002
– reference: Yousefpour A, Ishigaki G, Jue JP (2017) Fog computing: towards minimizing delay in the internet of things. In: 2017 IEEE International Conference on Edge Computing (EDGE). IEEE, pp 17–24
– reference: MahmoodZRamachandranMMahmoodZFog computing: concepts, principles and related paradigmsFog computing: concepts, frameworks and technologies, chap. 12018BerlinSpringer32110.1007/978-3-319-94890-4_1
– reference: Gia TN, Jiang M, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H (2015) Fog computing in healthcare internet of things: a case study on ECG feature extraction. In: 2015 IEEE International Conference on Computer and Information Technology. IEEE, pp 356–363
– reference: ChenLZhouSXuJComputation peer offloading for energy-constrained mobile edge computing in small-cell networksIEEE/ACM Trans Netw20182641619163210.1109/TNET.2018.2841758
– reference: TangZZhouXZhangFJiaWZhaoWMigration modeling and learning algorithms for containers in fog computingIEEE Trans Serv Comput201812571272510.1109/TSC.2018.2827070
– reference: Dastjerdi AV, Gupta H, Calheiros RN, Ghosh SK, Buyya R (2016) Fog computing: principles, architectures, and applications. In: Internet of things. Elsevier, pp 61–75
– reference: Mahmud R, Kotagiri R, Buyya R (2018) Fog computing: a taxonomy, survey and future directions. In: Internet of everything. Springer, pp 103–130
– reference: WangYShengMWangXWangLLiJMobile-edge computing: partial computation offloading using dynamic voltage scalingIEEE Trans Commun2016641042684282
– reference: SkarlatONardelliMSchulteSBorkowskiMLeitnerPOptimized IoT service placement in the fogSOCA20171142744310.1007/s11761-017-0219-8
– reference: MaoYYouCZhangJHuangKLetaiefKBA survey on mobile edge computing: the communication perspectiveIEEE Commun Surv Tutor20171942322235810.1109/COMST.2017.2745201
– reference: Zao JK, Gan TT, You CK, Méndez SJR, Chung CE, Te Wang Y, Mullen T, Jung TP (2014) Augmented brain computer interaction based on fog computing and linked data. In: 2014 International Conference on Intelligent Environments. IEEE, pp 374–377
– reference: PhanLANguyenDTLeeMParkDHKimTDynamic fog-to-fog offloading in SDN-based fog computing systemsFutur Gener Comput Syst202111748649710.1016/j.future.2020.12.021
– reference: LiLOtaKDongMDeep learning for smart industry: efficient manufacture inspection system with fog computingIEEE Trans Ind Inf201814104665467310.1109/TII.2018.2842821
– reference: Shah-MansouriHWongVWHierarchical fog-cloud computing for IoT systems: a computation offloading gameIEEE Internet Things J2018543246325710.1109/JIOT.2018.2838022
– reference: MahmoudMMRodriguesJJSaleemKAl-MuhtadiJKumarNKorotaevVTowards energy-aware fog-enabled cloud of things for healthcareComput Electr Eng201867586910.1016/j.compeleceng.2018.02.047
– reference: Yi S, Hao Z, Qin Z, Li Q (2015) Fog computing: platform and applications. In: 2015 third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb). IEEE, pp 73–78
– reference: Binh HTT, Anh TT, Son DB, Duc PA, Nguyen BM (2018) An evolutionary algorithm for solving task scheduling problem in cloud-fog computing environment. In: Proceedings of the Ninth International Symposium on Information and Communication Technology, SoICT 2018, p 397–404. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3287921.3287984
– reference: MachPBecvarZMobile edge computing: a survey on architecture and computation offloadingIEEE Commun Surv Tutor20171931628165610.1109/COMST.2017.2682318
– reference: MouradianCNaboulsiDYanguiSGlithoRHMorrowMJPolakosPAA comprehensive survey on fog computing: state-of-the-art and research challengesIEEE Commun Surv Tutor201820141646410.1109/COMST.2017.2771153
– reference: AazamMZeadallySHarrasKAOffloading in fog computing for IoT: review, enabling technologies, and research opportunitiesFuture Gener Comput Syst20188727828910.1016/j.future.2018.04.057
– reference: BadriHStochastic optimization methods for resource management in edge computing systems2019DetroitWayne State University
– reference: ShakaramiAGhobaei-AraniMShahidinejadAA survey on the computation offloading approaches in mobile edge computing: a machine learning-based perspectiveComput Netw202018210749610.1016/j.comnet.2020.107496
– reference: Zhao X, Zhao L, Liang K (2017) An energy consumption oriented offloading algorithm for fog computing. In: Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, pp 293–301. Springer. https://doi.org/10.1007/978-3-319-60717-7-29
– reference: MahmoodiSEUmaRNSubbalakshmiKPOptimal joint scheduling and cloud offloading for mobile applicationsIEEE Trans Cloud Comput20167230131310.1109/TCC.2016.2560808
– reference: AlliAAAlamMMThe fog cloud of things: a survey on concepts, architecture, standards, tools, and applicationsInternet Things2020910017710.1016/j.iot.2020.100177
– reference: GuBChenYLiaoHZhouZZhangDA distributed and context-aware task assignment mechanism for collaborative mobile edge computingSensors2018188242310.3390/s18082423
– reference: LiuLChangZGuoXMaoSRistaniemiTMultiobjective optimization for computation offloading in fog computingIEEE Internet Things J20185128329410.1109/JIOT.2017.2780236
– reference: NishaPFog computing and its real time applicationsInt J Emerg Technol Adv Eng201556266269
– reference: Chang Z, Zhou Z, Ristaniemi T, Niu Z (2017) Energy efficient optimization for computation offloading in fog computing system. In: GLOBECOM 2017–2017 IEEE Global Communications Conference. IEEE, pp 1–6
– reference: Wright KL (2019) High-performance distributed computing techniques for wireless IoT and connected vehicle systems. Ph.D. thesis, University of Southern California
– reference: AazamMSt-HilaireMLungCHLambadarisIHuhENIoT resource estimation challenges and modeling in Fog2018ChamSpringer173110.1007/978-3-319-57639-8-2
– reference: Mebrek A, Merghem-Boulahia L, Esseghir M (2017) Efficient green solution for a balanced energy consumption and delay in the IoT-fog-cloud computing. In: 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), pp 1–4 . https://doi.org/10.1109/NCA.2017.8171359
– reference: YousefpourAFungCNguyenTKadiyalaKJalaliFNiakanlahijiAKongJJueJPAll one needs to know about fog computing and related edge computing paradigms: a complete surveyJ Syst Architect20199828933010.1016/j.sysarc.2019.02.009
– reference: Daneshfar N, Pappas N, Polishchuk V, Angelakis V (2018) Service allocation in a mobile fog infrastructure under availability and QOS constraints. In: 2018 IEEE Global Communications Conference (GLOBECOM). IEEE, pp 1–6
– reference: SunXAnsariNLatency aware workload offloading in the cloudlet networkIEEE Commun Lett20172171481148410.1109/LCOMM.2017.2690678
– year: 2018
  ident: 3941_CR40
  publication-title: J Sens
  doi: 10.1155/2018/1386470
– volume: 6
  start-page: 3641
  issue: 2
  year: 2018
  ident: 3941_CR48
  publication-title: IEEE Internet Things J
  doi: 10.1109/JIOT.2018.2889511
– volume: 18
  start-page: 2423
  issue: 8
  year: 2018
  ident: 3941_CR76
  publication-title: Sensors
  doi: 10.3390/s18082423
– volume: 8
  start-page: 14
  issue: 4
  year: 2009
  ident: 3941_CR42
  publication-title: IEEE Pervasive Comput
  doi: 10.1109/MPRV.2009.82
– volume: 12
  start-page: 373
  issue: 4
  year: 2018
  ident: 3941_CR69
  publication-title: Enterprise Inf Syst
  doi: 10.1080/17517575.2017.1304579
– ident: 3941_CR34
  doi: 10.23919/INM.2017.7987464
– volume: 7
  start-page: 100070
  year: 2019
  ident: 3941_CR60
  publication-title: Internet Things
  doi: 10.1016/j.iot.2019.100070
– volume: 20
  start-page: 416
  issue: 1
  year: 2018
  ident: 3941_CR13
  publication-title: IEEE Commun Surv Tutor
  doi: 10.1109/COMST.2017.2771153
– volume: 98
  start-page: 289
  year: 2019
  ident: 3941_CR21
  publication-title: J Syst Architect
  doi: 10.1016/j.sysarc.2019.02.009
– ident: 3941_CR56
  doi: 10.1007/978-3-319-60717-7-29
– volume: 66
  start-page: 1594
  issue: 4
  year: 2018
  ident: 3941_CR65
  publication-title: IEEE Trans Commun
  doi: 10.1109/TCOMM.2017.2787700
– ident: 3941_CR66
  doi: 10.1145/2811587.2811598
– volume: 53
  start-page: 50
  issue: 4
  year: 2010
  ident: 3941_CR5
  publication-title: Commun ACM
  doi: 10.1145/1721654.1721672
– ident: 3941_CR57
  doi: 10.1109/GLOCOM.2017.8254207
– volume: 9
  start-page: 100177
  year: 2020
  ident: 3941_CR17
  publication-title: Internet Things
  doi: 10.1016/j.iot.2020.100177
– ident: 3941_CR2
– ident: 3941_CR55
  doi: 10.1109/ICDCS.2017.226
– year: 2019
  ident: 3941_CR11
  publication-title: ACM Comput Surv
  doi: 10.1145/3362031
– ident: 3941_CR25
– volume: 19
  start-page: 2322
  issue: 4
  year: 2017
  ident: 3941_CR51
  publication-title: IEEE Commun Surv Tutor
  doi: 10.1109/COMST.2017.2745201
– volume: 11
  start-page: 427
  year: 2017
  ident: 3941_CR71
  publication-title: SOCA
  doi: 10.1007/s11761-017-0219-8
– volume: 53
  start-page: 1
  issue: 3
  year: 2020
  ident: 3941_CR29
  publication-title: ACM Comput Surv
  doi: 10.1145/3391196
– year: 2020
  ident: 3941_CR18
  publication-title: ACM Comput Surv
  doi: 10.1145/3403955
– year: 2019
  ident: 3941_CR80
  publication-title: ACM Trans Internet Technol
  doi: 10.1145/3234463
– volume: 26
  start-page: 87
  issue: 1
  year: 2019
  ident: 3941_CR39
  publication-title: IEEE Wirel Commun
  doi: 10.1109/MWC.2019.1700441
– ident: 3941_CR44
  doi: 10.1016/j.dcan.2018.10.008
– volume: 67
  start-page: 58
  year: 2018
  ident: 3941_CR47
  publication-title: Comput Electr Eng
  doi: 10.1016/j.compeleceng.2018.02.047
– volume: 34
  start-page: 3590
  issue: 12
  year: 2016
  ident: 3941_CR67
  publication-title: IEEE J Sel Areas Commun
  doi: 10.1109/JSAC.2016.2611964
– volume: 6
  start-page: 47980
  year: 2018
  ident: 3941_CR19
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2018.2866491
– volume: 117
  start-page: 486
  year: 2021
  ident: 3941_CR15
  publication-title: Futur Gener Comput Syst
  doi: 10.1016/j.future.2020.12.021
– ident: 3941_CR6
  doi: 10.1145/2342509.2342513
– ident: 3941_CR9
  doi: 10.1109/ICFEC.2017.20
– volume: 87
  start-page: 278
  year: 2018
  ident: 3941_CR45
  publication-title: Future Gener Comput Syst
  doi: 10.1016/j.future.2018.04.057
– ident: 3941_CR30
  doi: 10.1002/cpe.5581
– volume: 21
  start-page: 1481
  issue: 7
  year: 2017
  ident: 3941_CR59
  publication-title: IEEE Commun Lett
  doi: 10.1109/LCOMM.2017.2690678
– volume: 9
  start-page: 306
  year: 2017
  ident: 3941_CR4
  publication-title: Veh Commun
– ident: 3941_CR81
– ident: 3941_CR1
– ident: 3941_CR41
  doi: 10.1109/SOSE.2013.73
– volume: 16
  start-page: 425
  issue: 3
  year: 2018
  ident: 3941_CR46
  publication-title: J Grid Comput
  doi: 10.1007/s10723-015-9356-5
– ident: 3941_CR70
  doi: 10.1109/CloudCom.2017.45
– volume-title: Stochastic optimization methods for resource management in edge computing systems
  year: 2019
  ident: 3941_CR86
– ident: 3941_CR35
  doi: 10.1109/CIT/IUCC/DASC/PICOM.2015.51
– ident: 3941_CR78
  doi: 10.3390/s19092122
– volume: 65
  start-page: 3702
  issue: 12
  year: 2016
  ident: 3941_CR74
  publication-title: IEEE Trans Comput
  doi: 10.1109/TC.2016.2536019
– year: 2017
  ident: 3941_CR23
  publication-title: J Netw Comput Appl
  doi: 10.1016/j.jnca.2017.09.002
– volume: 14
  start-page: e0224934
  issue: 11
  year: 2019
  ident: 3941_CR36
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0224934
– volume: 8
  start-page: 351
  issue: 6
  year: 2008
  ident: 3941_CR83
  publication-title: IJCSNS
– volume: 98
  start-page: 27
  year: 2017
  ident: 3941_CR20
  publication-title: J Netw Comput Appl
  doi: 10.1016/j.jnca.2017.09.002
– ident: 3941_CR27
  doi: 10.1145/3287921.3287984
– year: 2019
  ident: 3941_CR10
  publication-title: ACM Comput Surv
  doi: 10.1145/3326066
– volume: 182
  start-page: 107496
  year: 2020
  ident: 3941_CR16
  publication-title: Comput Netw
  doi: 10.1016/j.comnet.2020.107496
– volume: 19
  start-page: 1628
  issue: 3
  year: 2017
  ident: 3941_CR7
  publication-title: IEEE Commun Surv Tutor
  doi: 10.1109/COMST.2017.2682318
– ident: 3941_CR8
  doi: 10.1007/978-981-10-5861-5_5
– ident: 3941_CR88
– volume: 72
  start-page: 105
  issue: 1–2
  year: 2017
  ident: 3941_CR75
  publication-title: Ann Telecommun
  doi: 10.1007/s12243-016-0524-9
– volume: 18
  start-page: 1
  year: 2019
  ident: 3941_CR14
  publication-title: J Grid Comput
  doi: 10.1007/s10723-019-09491-1
– volume: 5
  start-page: 3246
  issue: 4
  year: 2018
  ident: 3941_CR61
  publication-title: IEEE Internet Things J
  doi: 10.1109/JIOT.2018.2838022
– ident: 3941_CR77
  doi: 10.1109/IEEE.EDGE.2017.12
– volume: 20
  start-page: 1826
  issue: 3
  year: 2018
  ident: 3941_CR22
  publication-title: IEEE Commun Surv Tutor
  doi: 10.1109/COMST.2018.2814571
– volume: 64
  start-page: 4268
  issue: 10
  year: 2016
  ident: 3941_CR64
  publication-title: IEEE Trans Commun
– ident: 3941_CR58
  doi: 10.1109/ACSSC.2015.7421170
– ident: 3941_CR37
  doi: 10.1109/NAS.2015.7255196
– start-page: 17
  volume-title: IoT resource estimation challenges and modeling in Fog
  year: 2018
  ident: 3941_CR43
  doi: 10.1007/978-3-319-57639-8-2
– volume: 52
  start-page: 71
  year: 2019
  ident: 3941_CR24
  publication-title: Pervasive Mobile Comput
  doi: 10.1016/j.pmcj.2018.12.007
– volume: 67
  start-page: 12452
  issue: 12
  year: 2018
  ident: 3941_CR31
  publication-title: IEEE Trans Veh Technol
  doi: 10.1109/TVT.2018.2871414
– volume: 26
  start-page: 1619
  issue: 4
  year: 2018
  ident: 3941_CR63
  publication-title: IEEE/ACM Trans Netw
  doi: 10.1109/TNET.2018.2841758
– ident: 3941_CR72
  doi: 10.1109/SOCA.2016.10
– volume: 19
  start-page: 1
  issue: 1
  year: 2018
  ident: 3941_CR52
  publication-title: ACM Trans Internet Technol
  doi: 10.1145/3186592
– volume: 7
  start-page: 301
  issue: 2
  year: 2016
  ident: 3941_CR50
  publication-title: IEEE Trans Cloud Comput
  doi: 10.1109/TCC.2016.2560808
– volume: 14
  start-page: 4665
  issue: 10
  year: 2018
  ident: 3941_CR53
  publication-title: IEEE Trans Ind Inf
  doi: 10.1109/TII.2018.2842821
– volume: 12
  start-page: 712
  issue: 5
  year: 2018
  ident: 3941_CR79
  publication-title: IEEE Trans Serv Comput
  doi: 10.1109/TSC.2018.2827070
– ident: 3941_CR49
  doi: 10.1145/3167132.3167215
– volume: 52
  start-page: 615
  issue: 4
  year: 2017
  ident: 3941_CR54
  publication-title: SIGPLAN Not.
  doi: 10.1145/3093336.3037698
– ident: 3941_CR32
  doi: 10.1109/HotWeb.2015.22
– ident: 3941_CR87
– ident: 3941_CR68
  doi: 10.1109/NCA.2017.8171359
– volume: 7
  start-page: 149182
  year: 2019
  ident: 3941_CR85
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2019.2946683
– start-page: 3
  volume-title: Fog computing: concepts, frameworks and technologies, chap. 1
  year: 2018
  ident: 3941_CR3
  doi: 10.1007/978-3-319-94890-4_1
– volume: 5
  start-page: 283
  issue: 1
  year: 2018
  ident: 3941_CR62
  publication-title: IEEE Internet Things J
  doi: 10.1109/JIOT.2017.2780236
– ident: 3941_CR12
  doi: 10.1145/2757384.2757397
– ident: 3941_CR28
– ident: 3941_CR73
  doi: 10.1109/GLOCOM.2018.8647488
– ident: 3941_CR84
  doi: 10.1109/ICNN.1995.488968
– volume: 5
  start-page: 266
  issue: 6
  year: 2015
  ident: 3941_CR26
  publication-title: Int J Emerg Technol Adv Eng
– ident: 3941_CR33
  doi: 10.1016/B978-0-12-805395-9.00004-6
– ident: 3941_CR38
  doi: 10.1109/IE.2014.54
– volume-title: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence
  year: 1992
  ident: 3941_CR82
  doi: 10.7551/mitpress/1090.001.0001
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Snippet In recent years, fog computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource...
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SubjectTerms Cloud computing
Compilers
Computation offloading
Computer Science
Internet of Things
Interpreters
Network latency
Placement
Processor Architectures
Programming Languages
Storage facilities
Title A survey on computation offloading and service placement in fog computing-based IoT
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