Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Mobility aware autonomic approach for the migration of application modules in fog computing environment

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

The fog computing paradigm has emanated as a widespread computing technology to support the execution of the internet of things applications. The paradigm introduces a distributed, hierarchical layer of nodes collaboratively working together as the Fog layer. User devices connected to Fog nodes are often non-stationary. The location-aware attribute of Fog computing, deems it necessary to provide uninterrupted services to the users, irrespective of their locations. Migration of user application modules among the Fog nodes is an efficient solution to tackle this issue. In this paper, an autonomic framework MAMF, is proposed to perform migrations of containers running user modules, while satisfying the Quality of Service requirements. The hybrid framework employing MAPE loop concepts and Genetic Algorithm, addresses the migration of containers in the Fog environment, while ensuring application delivery deadlines. The approach uses the pre-determined value of user location for the next time instant, to initiate the migration process. The framework was modelled and evaluated in iFogSim toolkit. The re-allocation problem was also mathematically modelled as an Integer Linear Programming problem. Experimental results indicate that the approach offers an improvement in terms of network usage, execution cost and request execution delay, over the existing approaches.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://www.virtuozzo.com/connect/details/blog/view/live-migration-in-virtuozzo-7.html.

References

  • Barcelo M, Correa A, Llorca J, Tulino AM, Vicario JL, Morell A (2016) Iot-cloud service optimization in next generation smart environments. IEEE J Selected Areas Commun 34(12):4077–4090

    Article  Google Scholar 

  • Bellavista P, Zanni A (2017)Feasibility of fog computing deployment based on docker containerization over Raspberrypi. In: Proceedings of the 18th international conference on distributed computing and networking. ACM, p 16

  • Bi Y, Han G, Lin C, Deng Q, Guo L, Li F (2018) Mobility support for fog computing: an SDN approach. IEEE Commun Mag 56(5):53–59

    Article  Google Scholar 

  • Bittencourt LF, Diaz-Montes J, Buyya R, Rana OF, Parashar M (2017) Mobility-aware application scheduling in fog computing. IEEE Cloud Comput 4(2):26–35

    Article  Google Scholar 

  • Bittencourt LF, Lopes MM, Petri I, Rana OF (2015) Towards virtual machine migration in fog computing. In: P2P, parallel, grid, cloud and internet computing (3PGCIC), 2015 10th international conference on. IEEE, pp 1–8

  • Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog computing: a platform for internet of things and analytics. In: Big data and internet of things: a roadmap for smart environments. Springer, Cham, pp 169–186

    Chapter  Google Scholar 

  • Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250

    Article  Google Scholar 

  • Dastjerdi AV, Gupta H, Calheiros RN, Ghosh SK, Buyya R (2016) Fog computing: principles, architectures, and applications. In: Internet of things. Morgan Kaufmann, pp 61–75

  • Friis HT (1946) A note on a simple transmission formula. Proc IRE 34(5):254–256

    Article  Google Scholar 

  • Gearhart JL, Adair KL, Detry RJ, Durfee JD, Jones KA, Martin N (2013) Comparison of open-source linear programming solvers. Tech Rep SAND2013-8847

  • Ghobaei-Arani M, Jabbehdari S, Pourmina MA (2016) An autonomic approach for resource provisioning of cloud services. Cluster Comput 19(3):1017–1036

    Article  Google Scholar 

  • Gozálvez J, Sepulcre M, Bauza R (2012) IEEE 802.11 p vehicle to infrastructure communications in urban environments. IEEE Commun Mag 50(5):176–183

    Article  Google Scholar 

  • Guerrero C, Lera I, Juiz C (2018) Genetic algorithm for multi-objective optimization of container allocation in cloud architecture. J Grid Comput 16(1):113–135

    Article  Google Scholar 

  • Guerrero C, Lera I, Juiz C (2019) A lightweight decentralized service placement policy for performance optimization in fog computing. J Ambient Intell Human Comput 10(6):2435–2452

    Article  Google Scholar 

  • Gupta H, Vahid Dastjerdi A, Ghosh SK, Buyya R (2017) ifogsim: a toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Softw Pract Exp 47(9):1275–1296

    Article  Google Scholar 

  • Hoque S, de Brito MS, Willner A, Keil O, Magedanz T (2017) Towards container orchestration in fog computing infrastructures. In: Computer software and applications conference (COMPSAC), 2017 IEEE 41st annual. IEEE, pp 294–299

  • Islam M, Razzaque A, Islam J (2016) A genetic algorithm for virtual machine migration in heterogeneous mobile cloud computing. In: Networking systems and security (NSysS), 2016 international conference on. IEEE, pp 1–6

  • Jacob B, Lanyon-Hogg R, Nadgir DK, Yassin AF (2004) A practical guide to the ibm autonomic computing toolkit. IBM Redbooks 4:10

    Google Scholar 

  • Kaur K, Dhand T, Kumar N, Zeadally S (2017) Container-as-a-service at the edge: trade-off between energy efficiency and service availability at fog nano data centers. IEEE Wireless Commun 24(3):48–56

    Article  Google Scholar 

  • Lèbre M-A, Le Mouël F, Ménard E (2015) Microscopic vehicular mobility trace of europarc roundabout, creteil, france. Open data trace, v1.0, Creative Commons Attribution-NonCommercial 4.0 International License

  • Liao S, Li J, Wu J, Yang W, Guan Z (2019) Fog-enabled vehicle as a service for computing geographical migration in smart cities. IEEE Access 7:8726–8736

    Article  Google Scholar 

  • Lopes MM, Higashino WA, Capretz MA, Bittencourt LF (2017) Myifogsim: a simulator for virtual machine migration in fog computing. In: Companion proceedings of the10th international conference on utility and cloud computing. ACM, pp 47–52

  • Machen A, Wang S, Leung KK, Ko BJ, Salonidis T (2018) Live service migration in mobile edge clouds. IEEE Wireless Commun 25(1):140–147

    Article  Google Scholar 

  • Mahmud R, Ramamohanarao K, Buyya R (2019a) Latency-aware application module management for fog computing environments. ACM Trans Internet Technol (TOIT) 19(1):9

    Article  Google Scholar 

  • Mahmud R, Srirama SN, Ramamohanarao K, Buyya R (2019b) Quality of experience (QOE)-aware placement of applications in fog computing environments. J Parallel Distrib Comput 132:190–203

    Article  Google Scholar 

  • Makridakis S, Andersen A, Carbone R, Fildes R, Hibon M, Lewandowski R, Newton J, Parzen E, Winkler R (1982) The accuracy of extrapolation (time series) methods: results of a forecasting competition. J Forecast 1(2):111–153

    Article  Google Scholar 

  • Marín-Tordera E, Masip-Bruin X, García-Almiñana J, Jukan A, Ren G-J, Zhu J (2017) Do we all really know what a fog node is? Current trends towards an open definition. Computer Commun 109:117–130

    Article  Google Scholar 

  • Martin JP, Kandasamy A, Chandrasekaran K (2018) Exploring the support for high performance applications in the container runtime environment. Hum Centric Comput Inf Sci 8(1):1

    Article  Google Scholar 

  • Martin JP, Kandasamy A, Chandrasekaran K, Joseph CT (2019) Elucidating the challenges for the praxis of fog computing: an aspect-based study. Int J Commun Syst 32(7):e3926

    Article  Google Scholar 

  • Mishra M, Roy SK, Mukherjee A, De D, Ghosh SK, Buyya R (2019) An energy-aware multi-sensor geo-fog paradigm for mission critical applications. J Ambient Intell Human Comput 1–19

  • Mitchell M (1998) An introduction to genetic algorithms. MIT Press, Cambridge

    Book  MATH  Google Scholar 

  • Shcherbakov MV, Brebels A, Shcherbakova NL, Tyukov AP, Janovsky TA, Kamaev VA (2013) A survey of forecast error measures. World Appl Sci J 24(2013):171–176

    Google Scholar 

  • Skarlat O, Nardelli M, Schulte S, Borkowski M, Leitner P (2017) Optimized iot service placement in the fog. Service Oriented Comput Appl 11(4):427–443

    Article  Google Scholar 

  • Talaat FM, Saraya MS, Saleh AI, Ali HA, Ali SH (2020) A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment. J Ambient Intell Human Comput 1–16

  • Verma P, Sood SK, Kalra S (2018) Cloud-centric iot based student healthcare monitoring framework. J Ambient Intell Human Comput 9(5):1293–1309

    Article  Google Scholar 

  • Zhu C, Pastor G, Xiao Y, Li Y, Ylae-Jaeaeski A (2018) Fog following me: Latency and quality balanced task allocation in vehicular fog computing. In: 2018 15th annual IEEE international conference on sensing, communication, and networking (SECON). IEEE, pp 1–9

Download references

Acknowledgements

This research is an outcome of the R&D project work undertaken under the Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation. We would like to thank the reviewers for the suggestions and comments made which have helped us to improve our work.

Author information

Authors and Affiliations

  1. Department of MACS, National Institute of Technology Karnataka, Surathkal, India

    John Paul Martin & A Kandasamy

  2. Department of CSE, National Institute of Technology Karnataka, Surathkal, India

    K Chandrasekaran

Authors
  1. John Paul Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A Kandasamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K Chandrasekaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John Paul Martin.

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

Martin, J.P., Kandasamy, A. & Chandrasekaran, K. Mobility aware autonomic approach for the migration of application modules in fog computing environment. J Ambient Intell Human Comput 11, 5259–5278 (2020). https://doi.org/10.1007/s12652-020-01854-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-01854-x

Keywords

Search

Navigation