Skip to main content
Log in

A survey on reliability and availability modeling of edge, fog, and cloud computing

  • Original Article
  • Published:
Journal of Reliable Intelligent Environments Aims and scope Submit manuscript

Abstract

During the past years, sending data to the cloud servers was a prominent trend, making the cloud computing paradigm dominate the technology landscape. However, the internet of things (IoT) is becoming a part of our daily environments, and it generates a large volume of data, which is creating uncontrollable delays. For the delay-sensitive and context-aware services, these uncontrollable delays may cause low reliability and availability for applications. To overcome these challenges, computing paradigms are moving from centralized cloud environments to the Edge of the networks. Several new computing paradigms, such as Edge and Fog computing, emerged to support delay-sensitive and context-aware services. By combining edge devices, fog servers, and cloud computing, companies can build a hierarchical IoT infrastructure, using Edge–Fog–Cloud orchestrated architecture to improve IoT environments’ performance, reliability, and availability. This paper presents a comprehensive survey on reliability and availability of Edge, Fog, and Cloud computing architectures. We first introduce and compare some related works about these paradigms and compare them to define the differences between Edge and Fog environments, since there is still some confusion about these terms. We also describe their taxonomy and how they link to each other. Finally, we draw some potential research directions that may help foster research efforts in this area.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Addis B, Ardagna D, Panicucci B, Squillante MS, Zhang L (2013) A hierarchical approach for the resource management of very large cloud platforms. IEEE Trans Dependable Secure Comput 10(5):253–272

    Article  Google Scholar 

  2. Ahmed M, Chowdhury ASMR, Ahme M, Rafee MMH (2012) An advanced survey on cloud computing and state-of-the-art research issues. Int J Comput Sci Issues (IJCSI) 9(1):201

    Google Scholar 

  3. Andrade E, Nogueira B, de Farias JI, Araújo D (2020) Performance and availability trade-offs in fog-cloud iot environments. J Netw Syst Manag 29(1):1–27

    Google Scholar 

  4. Angin P, Bhargava B, Jin Z (2015) A self-cloning agents based model for high-performance mobile-cloud computing. In: 2015 IEEE 8th international conference on cloud computing, IEEE, pp 301–308

  5. Ataie E, Entezari-Maleki R, Rashidi L, Trivedi KS, Ardagna D, Movaghar A (2017) Hierarchical stochastic models for performance, availability, and power consumption analysis of iaas clouds. IEEE Transactions on Cloud. Computing

  6. Avizienis A, Laprie JC, Randell B (2001) Fundamental concepts of computer system dependability. In: Workshop on robot dependability: technological challenge of dependable robots in human environments, Citeseer, pp 1–16

  7. Avizienis A, Laprie JC, Randell B, Landwehr CE (2004) Basic concepts and taxonomy of dependable and secure computing. IEEE Trans Dependable Sec Comput 1(1):11–33

    Article  Google Scholar 

  8. 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

  9. Boukerche A, Soto V (2020) An efficient mobility-oriented retrieval protocol for computation offloading in vehicular edge multi-access network. IEEE Trans Intell Transp Syst

  10. Chiang M, Ha S, Chih-Lin I, Risso F, Zhang T (2017) Clarifying fog computing and networking: 10 questions and answers. IEEE Commun Magn 55(4):18–20

    Article  Google Scholar 

  11. Consortium O et al (2017) Openfog reference architecture for fog computing. Architecture Working Group pp 1–162

  12. Dantas J, Matos R, Araujo J, Maciel P (2012) An availability model for eucalyptus platform: An analysis of warm-standy replication mechanism. In: 2012 IEEE international conference on Systems, man, and cybernetics (SMC), IEEE, pp 1664–1669

  13. Dantas J, Matos R, Araujo J, Maciel P (2015) Eucalyptus-based private clouds: availability modeling and comparison to the cost of a public cloud. Computing 97(11):1121–1140

    Article  MathSciNet  MATH  Google Scholar 

  14. Dantas J, Araujo E, Maciel P, Matos R, Teixeira J (2020) Estimating capacity-oriented availability in cloud systems. Int J Comput Sci Eng 22(4):466–476

    Google Scholar 

  15. 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

  16. Dehnavi S, Faragardi HR, Kargahi M, Fahringer T (2019) A reliability-aware resource provisioning scheme for real-time industrial applications in a fog-integrated smart factory. Microprocess Microsyst 70:1–14

    Article  Google Scholar 

  17. Di Mauro M, Galatro G, Longo M, Postiglione F, Tambasco M (2019) Ip multimedia subsystem in a containerized environment: availability and sensitivity evaluation. In: 2019 IEEE conference on network softwarization (NetSoft), IEEE, pp 42–47

  18. d’Oro EC, Colombo S, Gribaudo M, Iacono M, Manca D, Piazzolla P (2019) Modeling and evaluating a complex edge computing based systems: an emergency management support system case study. Internet of Things 6:100054

  19. Ericson CA, Ll C (1999) Fault tree analysis. System Safety Conference, Orlando, Florida 1:1–9

  20. Ever E, Shah P, Mostarda L, Omondi F, Gemikonakli O (2019) On the performance, availability and energy consumption modelling of clustered iot systems. Computing 101(12):1935–1970

    Article  MathSciNet  Google Scholar 

  21. Facchinetti D, Psaila G, Scandurra P (2019) Mobile cloud computing for indoor emergency response: the ipsos assistant case study. J Reliab Intell Environ 5(3):173–191

    Article  Google Scholar 

  22. Gamatié A, Devic G, Sassatelli G, Bernabovi S, Naudin P, Chapman M (2019) Towards energy-efficient heterogeneous multicore architectures for edge computing. IEEE Access 7:49474–49491

    Article  Google Scholar 

  23. German R (2000) Performance analysis of communication systems - modelling with non-Markovian stochastic Petri nets. Wiley-Interscience series in systems and optimization, Wiley, Amsterdam

  24. Ghosh R, Longo F, Xia R, Naik VK, Trivedi KS (2013) Stochastic model driven capacity planning for an infrastructure-as-a-service cloud. IEEE Trans Serv Comput 7(4):667–680

    Article  Google Scholar 

  25. Ghosh R, Longo F, Frattini F, Russo S, Trivedi KS (2014) Scalable analytics for iaas cloud availability. IEEE Trans Cloud Comput 2(1):57–70

    Article  Google Scholar 

  26. Gorbenko A, Romanovsky A, Tarasyuk O (2019) Fault tolerant internet computing: Benchmarking and modelling trade-offs between availability, latency and consistency. J Netw Comput Appl 146:102412

    Article  Google Scholar 

  27. Goyal A, Lavenberg SS (1987) Modeling and analysis of computer system availability. IBM J Res Dev 31(6):651–664

    Article  Google Scholar 

  28. Guan S, De Grande RE, Boukerche A (2016) A novel energy efficient platform based model to enable mobile cloud applications. In: 2016 IEEE Symposium on Computers and Communication (ISCC), IEEE, pp 914–919

  29. Ha K, Chen Z, Hu W, Richter W, Pillai P, Satyanarayanan M (2014) Towards wearable cognitive assistance. In: Proceedings of the 12th annual international conference on Mobile systems, applications, and services, pp 68–81

  30. Hardesty L (2017) Fog computing group publishes reference architecture

  31. Hayes B (2008) Cloud computing

  32. Huang CF, Huang DH, Lin YK (2020a) Network reliability evaluation for a distributed network with edge computing. Comput Ind Eng 147:106492

    Article  Google Scholar 

  33. Huang J, Liang J, Ali S (2020b) A simulation-based optimization approach for reliability-aware service composition in edge computing. IEEE Access 8:50355–50366

    Article  Google Scholar 

  34. Jammal M, Kanso A, Shami A (2015) Chase: component high availability-aware scheduler in cloud computing environment. In: 2015 IEEE 8th international conference on cloud computing, IEEE, pp 477–484

  35. Jayashree L, Selvakumar G (2020) Edge computing in iot. In: Getting started with enterprise internet of things: design approaches and software architecture models, Springer, pp 49–69

  36. Jia C, Lin K, Deng J (2020) A multi-property method to evaluate trust of edge computing based on data driven capsule network. In: IEEE INFOCOM 2020-IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE, pp 616–621

  37. Laprie JC (1992) Dependability: Basic concepts and terminology. In: Dependability: basic concepts and terminology, Springer, pp 3–245

  38. Jw L, Jang G, Jung H, Lee JG, Lee U (2019) Maximizing mapreduce job speed and reliability in the mobile cloud by optimizing task allocation. Pervasive Mob Comput 60:101082

    Article  Google Scholar 

  39. Li C, Wang Y, Tang H, Zhang Y, Xin Y, Luo Y (2019) Flexible replica placement for enhancing the availability in edge computing environment. Comput Commun 146:1–14

    Article  Google Scholar 

  40. Li J, Zhang T, Jin J, Yang Y, Yuan D, Gao L (2017) Latency estimation for fog-based internet of things. In: 2017 27th International telecommunication networks and applications conference (ITNAC), IEEE, pp 1–6

  41. Li S, Huang J (2017) Gspn-based reliability-aware performance evaluation of iot services. In: 2017 IEEE international conference on services computing (SCC), IEEE, pp 483–486

  42. Liang W, Ma Y, Xu W, Jia X, Chau SCK (2020) Reliability augmentation of requests with service function chain requirements in mobile edge-cloud networks. In: 49th International Conference on Parallel Processing - ICPP, Association for Computing Machinery, New York, NY, USA, ICPP ’20

  43. Liu B, Chang X, Han Z, Trivedi K, Rodríguez RJ (2018) Model-based sensitivity analysis of iaas cloud availability. Fut Gen Comput Syst 83:1–13

    Article  Google Scholar 

  44. Liu Y, Wang K, Ge L, Ye L, Cheng J (2019) Adaptive evaluation of virtual machine placement and migration scheduling algorithms using stochastic petri nets. IEEE Access 7:79810–79824

    Article  Google Scholar 

  45. Longo F, Ghosh R, Naik VK, Trivedi KS (2011) A scalable availability model for infrastructure-as-a-service cloud. In: 2011 IEEE/IFIP 41st international conference on dependable systems & networks (DSN), IEEE, pp 335–346

  46. Machida F, Andrade E, Kim DS, Trivedi KS (2011) Candy: Component-based availability modeling framework for cloud service management using sysml. In: 2011 IEEE 30th international symposium on reliable distributed systems, IEEE, pp 209–218

  47. Maciel P, Trivedi KS, Matias R, Kim DS (2011) Dependability modeling. Performance and dependability in service computing: concepts, techniques and research directions. IGI Global, Hershey

    Google Scholar 

  48. Mahmood Z, Ramachandran M (2018) Fog computing: concepts, principles and related paradigms. In: Fog Computing, Springer, pp 3–21

  49. Malhotra M, Trivedi KS (1994) Power-hierarchy of dependability-model types. IEEE Trans Reliab 43(3):493–502

    Article  Google Scholar 

  50. Mao K, Zhu Y, Chen Z, Tao X, Xue Q, Wu H, Mao Y, Hou J (2017) A visual model-based evaluation framework of cloud-based prognostics and health management. In: 2017 IEEE international conference on smart cloud (SmartCloud), IEEE, pp 33–40

  51. Marsan MA, Balbo G, Conte G, Donatelli S, Franceschinis G (1994) Modelling with generalized stochastic petri nets. Wiley, New York

    MATH  Google Scholar 

  52. Matos R, Araujo J, Oliveira D, Maciel P, Trivedi K (2015) Sensitivity analysis of a hierarchical model of mobile cloud computing. Simul Model Pract Theory 50:151–164

    Article  Google Scholar 

  53. Matos R, Dantas J, Araujo J, Trivedi KS, Maciel P (2017) Redundant eucalyptus private clouds: availability modeling and sensitivity analysis. J Grid Comput 15(1):1–22

    Article  Google Scholar 

  54. Mell P, Grance T et al (2011) The nist definition of cloud computing. Computer Security Division, Information Technology Laboratory, National

    Book  Google Scholar 

  55. Melo C, Dantas J, Oliveira D, Fé I, Matos R, Dantas R, Maciel R, Maciel P (2018) Dependability evaluation of a blockchain-as-a-service environment. In: 2018 IEEE symposium on computers and communications (ISCC), IEEE, pp 00909–00914

  56. Melo C, Dantas J, Maciel R, Silva P, Maciel P (2019) Models to evaluate service provisioning over cloud computing environments-a blockchain-as-a-service case study. Revista de Informática Teórica e Aplicada 26(3):65–74

    Article  Google Scholar 

  57. Melo C, Dantas J, Maciel P, Oliveira DM, Araujo J, Matos R, Fé I (2020) Models for hyper-converged cloud computing infrastructures planning. Int J Grid Util Comput 11(2):196–208

    Article  Google Scholar 

  58. Menascé DA, Almeida VA, Dowdy LW (2004) Performance by design: computer capacity planning by example. Prentice Hall PTR, New York

    Google Scholar 

  59. Molloy MK (1982) On the integration of delay and throughput measures in distributed processing models

  60. Molloy MK (1982) Performance analysis using stochastic petri nets. IEEE Trans Comput 31(9):913–917. https://doi.org/10.1109/TC.1982.1676110

    Article  Google Scholar 

  61. Murata T (1989) Petri nets: properties, analysis and applications. Proc IEEE 77(4):541–580

    Article  Google Scholar 

  62. 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

    Article  Google Scholar 

  63. Natkin SO (1980) Les Reseaux de petri stochastiques et leur application de l’evaluation des systemes informatiques

  64. Nguyen TA, Min D, Choi E (2020) A hierarchical modeling and analysis framework for availability and security quantification of iot infrastructures. Electronics 9(1):155

    Article  Google Scholar 

  65. Patel P, Ranabahu AH, Sheth AP (2009) Service level agreement in cloud computing

  66. Pereira P, Araujo J, Maciel P (2019) A hybrid mechanism of horizontal auto-scaling based on thresholds and time series. In: 2019 IEEE International Conference on Systems. Man and Cybernetics (SMC), IEEE, pp 2065–2070

  67. Pereira P, Araujo J, Torquato M, Dantas J, Melo C, Maciel P (2020) Stochastic performance model for web server capacity planning in fog computing. J Supercomput pp 1–25

  68. Pierce WH (2014) Failure-tolerant computer design. Academic Press, New York

    Google Scholar 

  69. Qiu X, Dai Y, Xiang Y, Xing L (2017) Correlation modeling and resource optimization for cloud service with fault recovery. IEEE Trans Cloud Comput

  70. Santos GL, Endo PT, da Silva Lisboa MFF, da Silva LGF, Sadok D, Kelner J, Lynn T et al (2018) Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructures. J Cloud Comput 7(1):16

    Article  Google Scholar 

  71. Sanyal S, Zhang P (2018) Improving quality of data: Iot data aggregation using device to device communications. IEEE Access 6:67830–67840

    Article  Google Scholar 

  72. Satyanarayanan M (2017) The emergence of edge computing. Computer 50(1):30–39

    Article  Google Scholar 

  73. Sharkh MA, Kalil M (2018) A quest for optimizing the data processing decision for cloud-fog hybrid environments. In: 2018 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, pp 1–6

  74. Sharma PK, Chen MY, Park JH (2017) A software defined fog node based distributed blockchain cloud architecture for iot. Ieee Access 6:115–124

    Article  Google Scholar 

  75. Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: vision and challenges. IEEE Int Things J 3(5):637–646

    Article  Google Scholar 

  76. Singh C, Billinton R (1977) System reliability, modelling and evaluation, vol 769. Hutchinson London

  77. Sun W, Liu J (2017) Coordinated multipoint-based uplink transmission in internet of things powered by energy harvesting. IEEE Int Things J 5(4):2585–2595

    Article  Google Scholar 

  78. Symons FJW (1989) Modelling and analysis of communication protocols using numerical petri nets

  79. Tian Y, Tian J, Li N (2020) Cloud reliability and efficiency improvement via failure risk based proactive actions. J Syst Softw 163:110524

    Article  Google Scholar 

  80. Trivedi KS, Hunter S, Garg S, Fricks R (1996) Reliability analysis techniques explored through a communication network example. North Carolina State University. Center for Advanced Computing and Communication Tech. rep

  81. Vahid Dastjerdi A, Gupta H, Calheiros RN, Ghosh SK, Buyya R (2016) Fog computing: principles, architectures, and applications. pp 1601

  82. Wang F, Wang X, Zhang C, He Q, Yang Y (2020) Fault tolerating multi-tenant service-based systems with dynamic quality. Knowl-Based Syst p 105715

  83. Wang T, Peng Z, Wen S, Lai Y, Jia W, Cai Y, Tian H, Chen Y (2017) Reliable wireless connections for fast-moving rail users based on a chained fog structure. Inf Sci 379:160–176

    Article  Google Scholar 

  84. Yakubu J, Christopher HA, Chiroma H, Abdullahi M et al (2019) Security challenges in fog-computing environment: a systematic appraisal of current developments. J Reliab Intell Environ 5(4):209–233

    Article  Google Scholar 

  85. 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

  86. Yousefpour A, Devic S, Nguyen BQ, Kreidieh A, Liao A, Bayen AM, Jue JP (2019) Guardians of the deep fog: failure-resilient dnn inference from edge to cloud. In: Proceedings of the first international workshop on challenges in artificial intelligence and machine learning for internet of things, association for computing machinery, New York, NY, USA, AIChallengeIoT’19, p 25–31

  87. 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 

  88. Zhou L, Guo H, Deng G (2019) A fog computing based approach to ddos mitigation in iiot systems. Comput Secur 85:51–62

    Article  Google Scholar 

  89. Zilic J, Aral A, Brandic I (2019) Efpo: Energy efficient and failure predictive edge offloading. In: Proceedings of the 12th IEEE/ACM international conference on utility and cloud computing, association for computing machinery, New York, NY, USA, UCC’19, pp 165–175

Download references

Acknowledgements

We would like to thank CAPES (the Brazilian Coordination of Improvement of Higher Education Personnel), CNPq (the Brazilian National Council for Scientific and Technological Development), FACEPE (Foundation Science and Technology Support of the State of Pernambuco) and MoDCS Research Group for their support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paulo Maciel.

Additional information

Publisher's Note

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

P. Maciel: www.cin.ufpe.br, www.modcs.org

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maciel, P., Dantas, J., Melo, C. et al. A survey on reliability and availability modeling of edge, fog, and cloud computing. J Reliable Intell Environ 8, 227–245 (2022). https://doi.org/10.1007/s40860-021-00154-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40860-021-00154-1

Keywords

Navigation