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Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructures

Authors:

Guto Leoni Santos,

Patricia Takako Endo,

Matheus Felipe Ferreira Da Silva Lisboa Tigre,

Leylane Graziele Ferreira Da Silva,

Theo LynnAuthors Info & Claims

Journal of Cloud Computing: Advances, Systems and Applications, Volume 7, Issue 1

Article No.: 118, Pages 1 - 22

https://doi.org/10.1186/s13677-018-0118-3

Published: 01 December 2018 Publication History

Abstract

The Internet of Things has the potential of transforming health systems through the collection and analysis of patient physiological data via wearable devices and sensor networks. Such systems can offer assisted living services in real-time and offer a range of multimedia-based health services. However, service downtime, particularly in the case of emergencies, can lead to adverse outcomes and in the worst case, death. In this paper, we propose an e-health monitoring architecture based on sensors that relies on cloud and fog infrastructures to handle and store patient data. Furthermore, we propose stochastic models to analyze availability and performance of such systems including models to understand how failures across the Cloud-to-Thing continuum impact on e-health system availability and to identify potential bottlenecks. To feed our models with real data, we design and build a prototype and execute performance experiments. Our results identify that the sensors and fog devices are the components that have the most significant impact on the availability of the e-health monitoring system, as a whole, in the scenarios analyzed. Our findings suggest that in order to identify the best architecture to host the e-health monitoring system, there is a trade-off between performance and delays that must be resolved.

References

[1]

Islam SR, Kwak D, Kabir MH, Hossain M, Kwak K-S (2015) The internet of things for health care: a comprehensive survey. IEEE Access 3:678---708.

[2]

Atzori L, Iera A, Morabito G (2010) The internet of things: A survey. Comput Netw 54(15):2787---2805.

Digital Library

[3]

Biswas AR, Giaffreda R (2014) Iot and cloud convergence: Opportunities and challenges In: Internet of Things (WF-IoT), 2014 IEEE World Forum On, 375---376. IEEE. https://www.computer.org/csdl/proceedings/wf-iot/2014/3459/00/06803194-abs.html.

[4]

Botta A, De Donato W, Persico V, Pescapé A (2014) On the integration of cloud computing and internet of things In: Future Internet of Things and Cloud (FiCloud), 2014 International Conference On, 23---30. IEEE. https://ieeexplore.ieee.org/abstract/document/6984170/.

Digital Library

[5]

Lee I, Lee K (2015) The internet of things (iot): Applications, investments, and challenges for enterprises. Bus Horiz 58(4):431---440.

[6]

Vaquero LM, Rodero-Merino L (2014) Finding your way in the fog: Towards a comprehensive definition of fog computing. ACM SIGCOMM Comput Commun Rev 44(5):27---32.

Digital Library

[7]

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, 13---16. ACM. https://dl.acm.org/citation.cfm?id=2342513.

Digital Library

[8]

Mell P, Grance TThe NIST definition of cloud computing. NIST Special Publication 800-145. 2011. http://faculty.winthrop.edu/domanm/csci411/Handouts/NIST.pdf.

Digital Library

[9]

Mshali H, Lemlouma T, Magoni D (2018) Adaptive monitoring system for e-health smart homes. Pervasive Mob Comput 43:1---19.

[10]

Trivedi K, Andrade E, Machida F (2012) Combining performance and availability analysis in practice In: Advances in Computers vol 84, 1---38. Elsevier. https://www.sciencedirect.com/science/article/pii/B9780123965257000010.

[11]

Zeng W, Koutny M, Watson P (2015) Opacity in internet of things with cloud computing (short paper) In: Service-Oriented Computing and Applications (SOCA), 2015 IEEE 8th International Conference On, 201---207. IEEE. https://ieeexplore.ieee.org/abstract/document/7399111/.

Digital Library

[12]

Colom JF, Mora H, Gil D, Signes-Pont MT (2017) Collaborative building of behavioural models based on internet of things. Comput Electr Eng 58:385---396.

Digital Library

[13]

Lomotey RK, Pry J, Sriramoju S (2017) Wearable iot data stream traceability in a distributed health information system. Pervasive Mob Comput 40:692---707.

Digital Library

[14]

Araujo J, Silva B, Oliveira D, Maciel P (2014) Dependability evaluation of a mhealth system using a mobile cloud infrastructure In: Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference On, 1348---1353. IEEE. https://ieeexplore.ieee.org/abstract/document/6974102/.

[15]

Li Y, Orgerie A-C, Rodero I, Parashar M, Menaud J-M (2017) Leveraging renewable energy in edge clouds for data stream analysis in iot In: Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 186---195. IEEE Press. https://ieeexplore.ieee.org/abstract/document/7973702/.

Digital Library

[16]

Souza VB, Masip-Bruin X, Marin-Tordera E, Ramírez W, Sanchez S (2016) Towards distributed service allocation in fog-to-cloud (f2c) scenarios In: Global Communications Conference (GLOBECOM), 2016 IEEE, 1---6. IEEE. https://ieeexplore.ieee.org/abstract/document/7842341/.

[17]

Bradley J, Reberger C, Dixit A, Gupta V (2013) Internet of everything: A $4.6 trillion public-sector opportunity. Whitepaper. https://www.cisco.com/c/dam/en_us/about/businessinsights/docs/ioe-public-sector-vas-white-paper.pdf.

[18]

Bradley J, Barbier J, Handle D (2013) Embracing the Internet of Everything To Capture Your Share of $14.4 Trillion. Whitepaper. https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoE_Economy.pdf.

[19]

Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: Vision and challenges. IEEE Internet Things J 3(5):637---646.

[20]

Chiang M, Zhang T (2016) Fog and iot: An overview of research opportunities. IEEE Internet Things J 3(6):854---864.

[21]

Iorga M, Feldman L, Barton R, Martin MJ, Goren N, Mahmoudi CThe NIST Definition of Fog Computing. NIST Special Publication 800-191 (Draft). 2017. https://csrc.nist.gov/csrc/media/publications/sp/800-191/draft/documents/sp800-191-draft.pdf.

[22]

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, 37---42. ACM. https://dl.acm.org/citation.cfm?id=2757397.

Digital Library

[23]

Luan TH, Gao L, Li Z, Xiang Y, Wei G, Sun L (2015) Fog computing: Focusing on mobile users at the edge. arXiv preprint arXiv:1502.01815. https://arxiv.org/abs/1502.01815.

[24]

Gissler B, Shrivastava P (2015) A system for design decisions based on reliability block diagrams In: Reliability and Maintainability Symposium (RAMS), 2015 Annual, 1---6. IEEE. https://ieeexplore.ieee.org/abstract/document/7105105/.

[25]

Hasan O, Ahmed W, Tahar S, Hamdi MS (2015) Reliability block diagrams based analysis: A survey In: AIP Conference Proceedings, vol 1648, 850129. AIP Publishing.

[26]

Signoret J-P, Dutuit Y, Cacheux P-J, Folleau C, Collas S, Thomas P (2013) Make your petri nets understandable: Reliability block diagrams driven petri nets. Reliab Eng Syst Saf 113:61---75.

[27]

Bourouni K (2013) Availability assessment of a reverse osmosis plant: comparison between reliability block diagram and fault tree analysis methods. Desalination 313:66---76.

[28]

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.

Digital Library

[29]

Labadi K, Benarbia T, Barbot J-P, Hamaci S, Omari A (2015) Stochastic petri net modeling, simulation and analysis of public bicycle sharing systems. IEEE Trans Autom Sci Eng 12(4):1380---1395.

[30]

Li P, Yang C, Xu H, LAU TF, Wang R (2017) User behaviour authentication model based on stochastic petri net in cloud environment In: International Symposium on Parallel Architecture, Algorithm and Programming, 59---69. Springer. https://link.springer.com/chapter/10.1007/978-981-10-6442-5_6.

[31]

Almutairi LM, Shetty S (2017) Generalized stochastic petri net model based security risk assessment of software defined networks In: Military Communications Conference (MILCOM), MILCOM 2017-2017 IEEE, 545---550. IEEE. https://ieeexplore.ieee.org/abstract/document/8170813/.

[32]

Heidary Z, Ghaisari J, Moein S, Naderi M, Gheisari Y (2016) Stochastic petri net modeling of hypoxia pathway predicts a novel incoherent feed-forward loop controlling sdf-1 expression in acute kidney injury. IEEE Trans Nanobioscience 15(1):19---26.

[33]

Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, Wagener T (2016) Sensitivity analysis of environmental models: A systematic review with practical workflow. Environ Model Softw 79:214---232.

Digital Library

[34]

Razavi S, Gupta HV (2015) What do we mean by sensitivity analysis? the need for comprehensive characterization of "global" sensitivity in earth and environmental systems models. Water Resour Res 51(5):3070---3092.

[35]

Hamby D (1994) A review of techniques for parameter sensitivity analysis of environmental models. Environ Monit Assess 32(2):135---154.

[36]

Chiuchisan I, Costin H-N, Geman O (2014) Adopting the internet of things technologies in health care systems In: Electrical and Power Engineering (EPE), 2014 International Conference and Exposition On, 532---535. IEEE. https://ieeexplore.ieee.org/abstract/document/6969965/.

[37]

Li H, Ota K, Dong M (2018) Learning iot in edge: Deep learning for the internet of things with edge computing. IEEE Netw 32(1):96---101.

[38]

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.

[39]

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.

Digital Library

[40]

Jain R (1990) The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling. John Wiley & Sons. https://www.wiley.com/en-us/The+Art+of+Computer+Systems+Performance+Analysis\%3A+Techniques+for+Experimental+Design%2C+Measurement%2C+Simulation%2C+and+Modeling-p-9780471503361.

[41]

Rahmati SHA, Ahmadi A, Sharifi M, Chambari A (2014) A multi-objective model for facility location---allocation problem with immobile servers within queuing framework. Comput Ind Eng 74:1---10.

Digital Library

[42]

Vilaplana J, Solsona F, Teixidó I, Mateo J, Abella F, Rius J (2014) A queuing theory model for cloud computing. J Supercomput 69(1):492---507.

Digital Library

[43]

Pham TN, Tsai M-F, Nguyen DB, Dow C-R, Deng D-J (2015) A cloud-based smart-parking system based on internet-of-things technologies. IEEE Access 3:1581---1591.

[44]

Al-Haidari F, Sqalli M, Salah K (2015) Evaluation of the impact of edos attacks against cloud computing services. Arab J Sci Eng 40(3):773---785.

[45]

Adhikary T, Das AK, Razzaque MA, Alrubaian M, Hassan MM, Alamri A (2017) Quality of service aware cloud resource provisioning for social multimedia services and applications. Multimedia Tools Appl 76(12):14485---14509.

Digital Library

[46]

Goldsztajn D, Ferragut A, Paganini F, Jonckheere M (2018) Controlling the number of active instances in a cloud environment. ACM SIGMETRICS Perform Eval Rev 45(2):15---20.

Digital Library

[47]

Yang B, Tan F, Dai Y-S, Guo S (2009) Performance evaluation of cloud service considering fault recovery In: IEEE International Conference on Cloud Computing, 571---576. Springer. https://link.springer.com/chapter/10.1007/978-3-642-10665-1_54.

Digital Library

[48]

Khazaei H, Misic J, Misic VB (2012) Performance analysis of cloud computing centers using m/g/m/m+ r queuing systems. IEEE Trans Parallel Distrib Syst 23(5):936---943.

Digital Library

[49]

Shelby Z, Hartke K, Bormann C (2014) The constrained application protocol (CoAP). No. RFC 7252. 2014. http://www.rfc-editor.org/info/rfc7252.

[50]

Shang W, Yu Y, Droms R, Zhang L (2016) Challenges in iot networking via tcp/ip architecture. Technical Report NDN-0038. NDN Project.

[51]

Chen X, Huang X, Jiao C, Flanner MG, Raeker T, Palen B (2017) Running climate model on a commercial cloud computing environment: A case study using community earth system model (cesm) on amazon aws. Comput Geosci 98:21---25.

Digital Library

[52]

Sagari S, Seskar I, Raychaudhuri D (2015) Modeling the coexistence of lte and wifi heterogeneous networks in dense deployment scenarios In: Communication Workshop (ICCW), 2015 IEEE International Conference On, 2301---2306. IEEE. https://ieeexplore.ieee.org/abstract/document/7247524/.

[53]

Araujo J, Maciel P, Torquato M, Callou G, Andrade E (2014) Availability evaluation of digital library cloud services In: Dependable Systems and Networks (DSN), 2014 44th Annual IEEE/IFIP International Conference On, 666---671. IEEE. https://ieeexplore.ieee.org/abstract/document/6903622/.

Digital Library

[54]

Silva B, Maciel P, Tavares E, Zimmermann A (2013) Dependability models for designing disaster tolerant cloud computing systems In: Dependable Systems and Networks (DSN), 2013 43rd Annual IEEE/IFIP International Conference On, 1---6. IEEE. https://ieeexplore.ieee.org/abstract/document/6575323/.

Digital Library

[55]

Tang D, Kumar D, Duvur S, Torbjornsen O (2004) Availability measurement and modeling for an application server In: Dependable Systems and Networks, 2004 International Conference On, 669---678. IEEE. https://ieeexplore.ieee.org/abstract/document/1311937/.

Digital Library

[56]

Kim DS, Machida F, Trivedi KS (2009) Availability modeling and analysis of a virtualized system In: Dependable Computing, 2009. PRDC'09. 15th IEEE Pacific Rim International Symposium On, 365---371. IEEE. https://ieeexplore.ieee.org/abstract/document/5368189/.

Digital Library

[57]

Novacek GTips for Predicting Product Reliability. http://circuitcellar.com/cc-blog/tips-for-predicting-product-reliability/. Accessed 05 Jan 2018.

[58]

Balc C, Cretu A, Munteanu R, Iudean D, Balan H, Karaisas P (2017) Reliability modeling for an automatic level control system In: Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP), 2017 International Conference On, 995---1000. IEEE. https://ieeexplore.ieee.org/abstract/document/7975100/.

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Published In

cover image Journal of Cloud Computing: Advances, Systems and Applications

Journal of Cloud Computing: Advances, Systems and Applications Volume 7, Issue 1

Apr 2018

431 pages

ISSN:2192-113X

EISSN:2192-113X

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Copyright © Copyright © 2018 The Author(s).

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Hindawi Limited

London, United Kingdom

Publication History

Published: 01 December 2018

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Cited By

Barbosa VSabino ALima LBrito CFeitosa LPereira PMaciel PNguyen TSilva F(2025)Performance Evaluation of IoT-Based Industrial Automation Using Edge, Fog, and Cloud ArchitecturesJournal of Network and Systems Management10.1007/s10922-024-09893-x33:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s10922-024-09893-x
Rocha FNogueira BGonçalves GSilva F(2024)Smart Hospital Patient Monitoring System Aided by Edge-Fog-Cloud Continuum: A Performability Evaluation Focusing on Distinct Sensor SourcesJournal of Network and Systems Management10.1007/s10922-024-09872-232:4Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1007/s10922-024-09872-2
Silva FFé ISilva FNguyen T(2024)Quantifying the impact of resource redundancy on smart city system dependability: a model-driven approachCluster Computing10.1007/s10586-023-04259-527:5(6059-6079)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10586-023-04259-5
Zeng YOuyang SZhu TLi C(2022)E-Commerce Network Security Based on Big Data in Cloud Computing EnvironmentMobile Information Systems10.1155/2022/99352442022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/9935244
Mas LVilaplana JMateo JSolsona F(2022)A queuing theory model for fog computingThe Journal of Supercomputing10.1007/s11227-022-04328-378:8(11138-11155)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s11227-022-04328-3
Li FShankar ASanthosh Kumar BBalamurugan SMuthu BPeng SAbd Wahab M(2021)Fog-Internet of things-assisted multi-sensor intelligent monitoring model to analyze the physical health conditionTechnology and Health Care10.3233/THC-21300929:6(1319-1337)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.3233/THC-213009
Sun XXu RWu LGuan Z(2021)A differentially private distributed data mining scheme with high efficiency for edge computingJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-020-00225-310:1Online publication date: 19-Jan-2021
https://dl.acm.org/doi/10.1186/s13677-020-00225-3
Santos GLynn TKelner JEndo P(2021)Availability-aware and energy-aware dynamic SFC placement using reinforcement learningThe Journal of Supercomputing10.1007/s11227-021-03784-777:11(12711-12740)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11227-021-03784-7
Santos LCunha BFé IVieira MSilva F(2021)Data Processing on Edge and Cloud: A Performability Evaluation and Sensitivity AnalysisJournal of Network and Systems Management10.1007/s10922-021-09592-x29:3Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s10922-021-09592-x
Andrade ENogueira BFarias Júnior IAraújo D(2021)Performance and Availability Trade-Offs in Fog–Cloud IoT EnvironmentsJournal of Network and Systems Management10.1007/s10922-020-09570-929:1Online publication date: 1-Jan-2021
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