We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Design and implementation of an IoT-cloud converged virtual machine system | SpringerLink


Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Design and implementation of an IoT-cloud converged virtual machine system


This paper presents an internet-of-things (IoT)-cloud converged virtual machine (VM) system for IoT devices with restricted computing resources. The VM is a software processor that has many advantages in terms of software development, release, maintenance, etc., owing to its platform independence. However, in low-performance devices it has a significant disadvantage because its use is restricted by high execution overheads at the software interpretation level. This paper proposes a VM system for IoT devices that solves this problem while retaining the advantages of VM technology using a lightweight interpreter model and cloud-based computation offloading. The proposed interpreter solves the limited memory/performance problem when running VMs on low-performance devices using two-level instruction-to-native function matching techniques. Furthermore, by solving the low-performance issues of IoT devices using cloud-based offloading, the proposed IoT-cloud VM can run applications that require high-performance computing even when the target hardware system is a low-power IoT device.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14


  1. 1.

    Son Y, Lee Y (2012) A study on the smart virtual machine for executing virtual machine codes on smart platforms. Int J Smart Home 6(4):93–105

  2. 2.

    Son Y, Lee Y (2013) A study on the smart virtual machine for smart devices. Inf Int Interdiscip J 16(2):1465–1472

  3. 3.

    Lee Y, Son Y (2014) Smart virtual machine code based compilers for supporting multi programming languages in smart cross platform. Int J Smart Home 8(5):249–260. https://doi.org/10.14257/ijseia.2014.8.5.20

  4. 4.

    Lee Y, Jeong J, Son Y (2017) Design and implementation of the secure compiler and virtual machine for developing secure IoT services. Future Gen Comput Syst 76:350–357. https://doi.org/10.1016/j.future.2016.03.014

  5. 5.

    Son Y, Kim JH, Lee Y (2014) A design and implementation of HTML5 based SVM for integrating runtime of smart devices and web environments. Int J Smart Home 8(3):223–234. https://doi.org/10.14257/ijsh.2014.8.3.21

  6. 6.

    Yang K, Ou S, Chen HH (2008) On effective offloading services for resource-constrained mobile devices running heavier mobile internet applications. IEEE Commun Mag 46(1):56–63. https://doi.org/10.1109/mcom.2008.4427231

  7. 7.

    Kumar K (2013) A survey of computation offloading for mobile systems. Mob Netw Appl 18(1):129–140. https://doi.org/10.1007/s11036-012-0368-0

  8. 8.

    Shi C (2014) Cosmos: computation offloading as a service for mobile devices. In: Proceedings of the 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp 287–296. https://doi.org/10.1145/2632951.2632958

  9. 9.

    Chun BG (2011) Clonecloud: elastic execution between mobile device and cloud. In: Proceedings of the 6th ACM Conference on Computer Systems, pp 301–314. https://doi.org/10.1145/1966445.1966473

  10. 10.

    Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mob Comput 13(18):1587–1611. https://doi.org/10.1002/wcm.1203

  11. 11.

    La H, Kim S (2014) A taxonomy of offloading in mobile cloud computing. In: Proceedings of the 7th IEEE International Conference on Service-Oriented Computing and Applications, pp 147–153. https://doi.org/10.1109/soca.2014.22

  12. 12.

    Wang C, Li Z (2004) A computation offloading scheme on handheld devices. J Parallel Distrib Comput 64(6):740–746. https://doi.org/10.1016/j.jpdc.2003.10.005

  13. 13.

    Chen H, Lin Y, Chen C (2012) COCA: computation offload to clouds using AOP. In: Proceedings of the 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp 466–473. https://doi.org/10.1109/ccgrid.2012.98

  14. 14.

    Lin T, Hsu C, King C (2013) Context-aware decision engine for mobile cloud offloading. In: Proceedings of IEEE Wireless Communications and Networking Conference Workshops, pp 111–116. https://doi.org/10.1109/wcncw.2013.6533324

  15. 15.

    Zeng D, Dai Y, Li F, Sherratt RS, Wang J (2018) Adversarial learning for distant supervised relation extraction. Comput Mater Contin 55(1):121–136. https://doi.org/10.3970/cmc.2018.055.121

  16. 16.

    Tu Y, Lin Y, Wang J, Kim JU (2018) Semi-supervised learning with generative adversarial networks on digital signal modulation classification. Comput Mater Contin 55(2):243–254. https://doi.org/10.3970/cmc.2018.01755

  17. 17.

    Son Y, Lee Y (2017) Offloading method for efficient use of local computational resources in mobile location-based services using clouds. Mob Inf Syst 2017:1–9. https://doi.org/10.1155/2017/1856329

  18. 18.

    Son Y, Jeong J, Lee Y (2018) An adaptive offloading method for an IoT-cloud converged virtual machine system using a hybrid deep neural network. Sustainability 10(11):3955

  19. 19.

    Kumar K, Lu Y (2010) Cloud computing for mobile users: Can offloading computation save energy? Computer 43(4):51–56. https://doi.org/10.1109/mc.2010.98

  20. 20.

    Cao D, Zheng B, Ji B, Lei Z, Feng C (2018) A robust distance-based relay selection for message dissemination in vehicular network. Wirel Netw 2018:1–17. https://doi.org/10.1007/s11276-018-1863-4

  21. 21.

    Liao Z, Liang J, Feng C (2017) Mobile relay deployment in multihop relay networks. Comput Commun 112(1):14–21. https://doi.org/10.1016/j.comcom.2017.07.008

  22. 22.

    Kovachev D, Yu T, Klamma R (2012) Computation offloading from mobile devices into the cloud. In: Proceedings of IEEE 10th International Symposium on Parallel and Distributed Processing with Applications, pp 784–791

  23. 23.

    Son Y, Lee Y (2011) Design and implementation of an objective-C compiler for the virtual machine on smart phone. Multimed Comput Graph Broadcast CCIS 262:52–59. https://doi.org/10.1007/978-3-642-27204-2_7

Download references


This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (No. 2016R1A2B4008392), and this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (No. 2017R1C1B5018257), and also this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1A5A7023490).

Author information

Correspondence to YangSun Lee.

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

Verify currency and authenticity via CrossMark

Cite this article

Son, Y., Jeong, J. & Lee, Y. Design and implementation of an IoT-cloud converged virtual machine system. J Supercomput (2019). https://doi.org/10.1007/s11227-019-02866-x

Download citation


  • Internet of things
  • Cloud system
  • Computational offloading
  • Virtual machine