Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. We use computation offloading and task offloading interchangeably throughout this paper, since both terms are used by different studies.

  2. We use service placement, application placement, and task placement interchangeably throughout this paper, since all expressions are used by the previous studies with the same meaning.

References

  1. 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/

  2. IDC: Iot growth demands rethink of long-term storage strategies (2020). https://www.idc.com/getdoc.jsp?containerId=prAP46737220

  3. Mahmood Z, Ramachandran M (2018) Fog computing: concepts, principles and related paradigms. In: Mahmood Z (ed.) Fog computing: concepts, frameworks and technologies, chap. 1. Springer, Berlin, pp. 3–21

    Chapter  Google Scholar 

  4. Daniel A, Subburathinam K, Paul A, Rajkumar N, Rho S (2017) Big autonomous vehicular data classifications: towards procuring intelligence. Veh Commun 9:306–312

    Google Scholar 

  5. Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I et al. (2010) A view of cloud computing. Commun ACM 53(4), 50–58

    Article  Google Scholar 

  6. 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

  7. Mach P, Becvar Z (2017) Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun Surv Tutor 19(3):1628–1656

    Article  Google Scholar 

  8. Mahmud R, Kotagiri R, Buyya R (2018) Fog computing: a taxonomy, survey and future directions. In: Internet of everything. Springer, pp 103–130

  9. 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

  10. Hong CH, Varghese B (2019) Resource management in fog/edge computing: a survey on architectures, infrastructure, and algorithms. ACM Comput Surv. https://doi.org/10.1145/3326066

    Article  Google Scholar 

  11. Ren J, Zhang D, He S, Zhang Y, Li T (2019) A survey on end-edge-cloud orchestrated network computing paradigms: transparent computing, mobile edge computing, fog computing, and cloudlet. ACM Comput Surv. https://doi.org/10.1145/3362031

    Article  Google Scholar 

  12. 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

  13. Mouradian C, Naboulsi D, Yangui S, Glitho RH, Morrow MJ, Polakos PA (2018) A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun Surv Tutor 20(1):416–464. https://doi.org/10.1109/COMST.2017.2771153

    Article  Google Scholar 

  14. Ghobaei-Arani M, Souri A, Rahmanian A (2019) Resource management approaches in fog computing: a comprehensive review. J Grid Comput 18:1–42

    Article  Google Scholar 

  15. Phan LA, Nguyen DT, Lee M, Park DH, Kim T (2021) Dynamic fog-to-fog offloading in sdn-based fog computing systems. Futur Gener Comput Syst 117:486–497

    Article  Google Scholar 

  16. Shakarami A, Ghobaei-Arani M, Shahidinejad A (2020) A survey on the computation offloading approaches in mobile edge computing: a machine learning-based perspective. Comput Netw 182:107496

    Article  Google Scholar 

  17. Alli AA, Alam MM (2020) The fog cloud of things: a survey on concepts, architecture, standards, tools, and applications. Internet Things 9:100177

    Article  Google Scholar 

  18. Mahmud R, Ramamohanarao K, Buyya R (2020) Application management in fog computing environments: a taxonomy, review and future directions. ACM Comput Surv. https://doi.org/10.1145/3403955

    Article  Google Scholar 

  19. Naha RK, Garg S, Georgakopoulos D, Jayaraman PP, Gao L, Xiang Y, Ranjan R (2018) Fog computing: survey of trends, architectures, requirements, and research directions. IEEE Access 6:47980–48009. https://doi.org/10.1109/ACCESS.2018.2866491

    Article  Google Scholar 

  20. Hu P, Dhelim S, Ning H, Qiu T (2017) Survey on fog computing: architecture, key technologies, applications and open issues. J Netw Comput Appl 98:27–42. https://doi.org/10.1016/j.jnca.2017.09.002

    Article  Google Scholar 

  21. Yousefpour A, Fung C, Nguyen T, Kadiyala K, Jalali F, Niakanlahiji A, Kong J, Jue JP (2019) All one needs to know about fog computing and related edge computing paradigms: a complete survey. J Syst Architect 98:289–330

    Article  Google Scholar 

  22. Mukherjee M, Shu L, Wang D (2018) Survey of fog computing: fundamental, network applications, and research challenges. IEEE Commun Surv Tutor 20(3):1826–1857. https://doi.org/10.1109/COMST.2018.2814571

    Article  Google Scholar 

  23. Hu P, Dhelim S, Ning H, Qiu T (2017) Survey on fog computing: architecture, key technologies, applications and open issues. J Netw Comput Appl. https://doi.org/10.1016/j.jnca.2017.09.002

    Article  Google Scholar 

  24. Bellavista P, Berrocal J, Corradi A, Das SK, Foschini L, Zanni A (2019) A survey on fog computing for the internet of things. Pervasive Mobile Comput 52:71–99

    Article  Google Scholar 

  25. 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

  26. Nisha P (2015) Fog computing and its real time applications. Int J Emerg Technol Adv Eng 5(6):266–269

    Google Scholar 

  27. 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

  28. 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

  29. Salaht FA, Desprez F, Lebre A (2020) An overview of service placement problem in fog and edge computing. ACM Comput Surv (CSUR) 53(3):1–35

    Article  Google Scholar 

  30. 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

  31. Ding H, Fang Y (2018) Virtual infrastructure at traffic lights: vehicular temporary storage assisted data transportation at signalized intersections. IEEE Trans Veh Technol 67(12):12452–12456. https://doi.org/10.1109/TVT.2018.2871414

    Article  Google Scholar 

  32. 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

  33. 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

  34. 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

  35. 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

  36. Shukla S, Hassan MF, Khan MK, Jung LT, Awang A (2019) An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment. PLoS ONE 14(11):e0224934

    Article  Google Scholar 

  37. 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

  38. 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

  39. Ning Z, Huang J, Wang X (2019) Vehicular fog computing: enabling real-time traffic management for smart cities. IEEE Wirel Commun 26(1):87–93

    Article  Google Scholar 

  40. Paul A, Pinjari H, Hong WH, Seo HC, Rho S (2018) Fog computing-based IoT for health monitoring system. J Sens. https://doi.org/10.1155/2018/1386470

    Article  Google Scholar 

  41. 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

  42. Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for vm-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23. https://doi.org/10.1109/MPRV.2009.82

    Article  Google Scholar 

  43. Aazam M, St-Hilaire M, Lung CH, Lambadaris I, Huh EN (2018) IoT resource estimation challenges and modeling in fog. Springer, Cham. https://doi.org/10.1007/978-3-319-57639-8-2

    Book  Google Scholar 

  44. 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

  45. Aazam M, Zeadally S, Harras KA (2018) Offloading in fog computing for IoT: review, enabling technologies, and research opportunities. Future Gener Comput Syst 87:278–289

    Article  Google Scholar 

  46. Aazam M, Huh EN, St-Hilaire M (2018) Towards media inter-cloud standardization-evaluating impact of cloud storage heterogeneity. J Grid Comput 16(3):425–443

    Article  Google Scholar 

  47. Mahmoud MM, Rodrigues JJ, Saleem K, Al-Muhtadi J, Kumar N, Korotaev V (2018) Towards energy-aware fog-enabled cloud of things for healthcare. Comput Electr Eng 67:58–69

    Article  Google Scholar 

  48. Lera I, Guerrero C, Juiz C (2018) Availability-aware service placement policy in fog computing based on graph partitions. IEEE Internet Things J 6(2):3641–3651

    Article  Google Scholar 

  49. 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

  50. Mahmoodi SE, Uma RN, Subbalakshmi KP (2016) Optimal joint scheduling and cloud offloading for mobile applications. IEEE Trans Cloud Comput 7(2):301–313

    Article  Google Scholar 

  51. Mao Y, You C, Zhang J, Huang K, Letaief KB (2017) A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutor 19(4):2322–2358. https://doi.org/10.1109/COMST.2017.2745201

    Article  Google Scholar 

  52. Mahmud R, Ramamohanarao K, Buyya R (2018) Latency-aware application module management for fog computing environments. ACM Trans Internet Technol (TOIT) 19(1):1–21

    Article  Google Scholar 

  53. Li L, Ota K, Dong M (2018) Deep learning for smart industry: efficient manufacture inspection system with fog computing. IEEE Trans Ind Inf 14(10):4665–4673. https://doi.org/10.1109/TII.2018.2842821

    Article  Google Scholar 

  54. Kang Y, Hauswald J, Gao C, Rovinski A, Mudge T, Mars J, Tang L (2017) Neurosurgeon: collaborative intelligence between the cloud and mobile edge. SIGPLAN Not. 52(4):615–629. https://doi.org/10.1145/3093336.3037698

    Article  Google Scholar 

  55. 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

  56. 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

  57. 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

  58. 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

  59. Sun X, Ansari N (2017) Latency aware workload offloading in the cloudlet network. IEEE Commun Lett 21(7), 1481–1484. https://doi.org/10.1109/LCOMM.2017.2690678

    Article  Google Scholar 

  60. Alli AA, Alam MM (2019) Secoff-fciot: machine learning based secure offloading in fog-cloud of things for smart city applications. Internet Things 7:100070

    Article  Google Scholar 

  61. Shah-Mansouri H, Wong VW (2018) Hierarchical fog-cloud computing for IoT systems: a computation offloading game. IEEE Internet Things J 5(4):3246–3257

    Article  Google Scholar 

  62. Liu L, Chang Z, Guo X, Mao S, Ristaniemi T (2018) Multiobjective optimization for computation offloading in fog computing. IEEE Internet Things J 5(1):283–294. https://doi.org/10.1109/JIOT.2017.2780236

    Article  Google Scholar 

  63. Chen L, Zhou S, Xu J (2018) Computation peer offloading for energy-constrained mobile edge computing in small-cell networks. IEEE/ACM Trans Netw 26(4):1619–1632. https://doi.org/10.1109/TNET.2018.2841758

    Article  Google Scholar 

  64. Wang Y, Sheng M, Wang X, Wang L, Li J (2016) Mobile-edge computing: partial computation offloading using dynamic voltage scaling. IEEE Trans Commun 64(10):4268–4282

    Google Scholar 

  65. Du J, Zhao L, Feng J, Chu X (2018) Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee. IEEE Trans Commun 66(4), 1594–1608

    Article  Google Scholar 

  66. 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

  67. Mao Y, Zhang J, Letaief KB (2016) Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE J Sel Areas Commun 34(12), 3590–3605

    Article  Google Scholar 

  68. 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

  69. Bitam S, Zeadally S, Mellouk A (2018) Fog computing job scheduling optimization based on bees swarm. Enterprise Inf Syst 12(4):373–397

    Article  Google Scholar 

  70. 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

  71. Skarlat O, Nardelli M, Schulte S, Borkowski M, Leitner P (2017) Optimized IoT service placement in the fog. SOCA 11:427–443

    Article  Google Scholar 

  72. 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

  73. 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

  74. Zeng D, Gu L, Guo S, Cheng Z, Yu S (2016) Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Trans Comput 65(12), 3702–3712. https://doi.org/10.1109/TC.2016.2536019

    Article  MathSciNet  MATH  Google Scholar 

  75. Velasquez K, Abreu DP, Curado M, Monteiro E (2017) Service placement for latency reduction in the internet of things. Ann Telecommun 72(1–2), 105–115

    Article  Google Scholar 

  76. Gu B, Chen Y, Liao H, Zhou Z, Zhang D (2018) A distributed and context-aware task assignment mechanism for collaborative mobile edge computing. Sensors 18(8):2423

    Article  Google Scholar 

  77. 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

  78. 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

  79. Tang Z, Zhou X, Zhang F, Jia W, Zhao W (2018) Migration modeling and learning algorithms for containers in fog computing. IEEE Trans Serv Comput 12(5), 712–725

    Article  Google Scholar 

  80. Li H, Ota K, Dong M (2019) Deep reinforcement scheduling for mobile crowdsensing in fog computing. ACM Trans Internet Technol. https://doi.org/10.1145/3234463

    Article  Google Scholar 

  81. Filippo Poltronieri Mauro Tortonesi CS, Sur N (2021) Reinforcement learning for value-based placement of fog services

  82. Holland JH et al. (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge

    Book  Google Scholar 

  83. Yaseen SG, Al-Slamy N (2008) Ant colony optimization. IJCSNS 8(6):351

    Google Scholar 

  84. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks. IEEE, vol 4, pp 1942–1948

  85. Wang J, Wu W, Liao Z, Sangaiah AK, Simon Sherratt R (2019) An energy-efficient off-loading scheme for low latency in collaborative edge computing. IEEE Access 7:149182–149190. https://doi.org/10.1109/ACCESS.2019.2946683

    Article  Google Scholar 

  86. Badri H (2019) Stochastic optimization methods for resource management in edge computing systems. Wayne State University, Detroit

    Google Scholar 

  87. Wright KL (2019) High-performance distributed computing techniques for wireless IoT and connected vehicle systems. Ph.D. thesis, University of Southern California

  88. Sundar S (2019) Optimization algorithms for task offloading and scheduling in cloud computing. Ph.D. thesis

Download references

Author information

Authors and Affiliations

  1. Higher Institute of Applied Languages and Computer Sciences in Beja, Tunis, Tunisia

    Kaouther Gasmi

  2. Graduate School of Science and Engineering, Hacettepe University, Beytepe, Ankara, Turkey

    Selma Dilek

  3. Department of Computer Engineering, Hacettepe University, Ankara, Turkey

    Suleyman Tosun & Suat Ozdemir

Authors
  1. Kaouther Gasmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Selma Dilek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suleyman Tosun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Suat Ozdemir
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Suleyman Tosun.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gasmi, K., Dilek, S., Tosun, S. et al. A survey on computation offloading and service placement in fog computing-based IoT. J Supercomput 78, 1983–2014 (2022). https://doi.org/10.1007/s11227-021-03941-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03941-y

Keywords

Search

Navigation