N2SkyC - User Friendly and Efficient Neural Network Simulation Fostering Cloud Containers

  • Aliaksandr Adamenko
  • Andrii Fedorenko
  • Erich SchikutaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)


Sky computing is a new computing paradigm leveraging resources of multiple Cloud providers to create a large scale distributed infrastructure. N2Sky is a research initiative promising a framework for the utilization of Neural Networks as services across many Clouds. This involves a number of challenges ranging from the provision, discovery and utilization of services to the management, monitoring, metering and accounting of the infrastructure.

Cloud Container technology offers fast deployment, good portability, and high resource efficiency to run large-scale and distributed systems. In recent years, container-based virtualization for applications has gained immense popularity.

This paper presents the new N2SkyC system, a framework for the utilization of Neural Networks as services, aiming for higher flexibility, portability, dynamic orchestration, and performance by fostering microservices and Cloud container technology.


Problem solving environment Neural networks Cloud computing Containers Microservices 


  1. 1.
    Beran, P.P., Vinek, E., Schikuta, E., Weishaupl, T.: Vinnsl - the Vienna neural network specification language. In: IEEE International Joint Conference on Neural Networks IJCNN 2008, IEEE World Congress on Computational Intelligence, pp. 1872–1879. IEEE (2008)Google Scholar
  2. 2.
    e-Science: UK e-science programme (2016). Accessed Jan 2018
  3. 3.
    Erfan Eshratifar, A., Saeed Abrishami, M., Pedram, M.: JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services, University of Southern California (2018)Google Scholar
  4. 4.
    Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: enabling scalable virtual organizations. Int. J. High Perform. Comput. Appl. 15(3), 200–222 (2001)CrossRefGoogle Scholar
  5. 5.
    Ghodsi, Z., Gu, T., Garg, S.: SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud, New York University (2017)Google Scholar
  6. 6.
    Guazzelli, A., Zeller, M., Lin, W.C., Williams, G., et al.: PMML: an open standard for sharing models. R J. 1(1), 60–65 (2009)Google Scholar
  7. 7.
    Huqqani, A.A., Li, X., Beran, P.P., Schikuta, E.: N2Cloud: cloud based neural network simulation application. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–5. IEEE (2010)Google Scholar
  8. 8.
    Keahey, K., Tsugawa, M., Matsunaga, A., Fortes, J.: Sky computing. IEEE Internet Comput. 13(5), 43–51 (2009)CrossRefGoogle Scholar
  9. 9.
    Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J.: Som pak: The self-organizing map program package. Report A31, Helsinki University of Technology, Laboratory of Computer and Information Science (1996)Google Scholar
  10. 10.
    Leighton, R.R., Wieland, A.: The aspirin/migraines software tools, user’s manual. Technical Report MP-91W00050 (1991)Google Scholar
  11. 11.
    Prieto, A., et al.: Neural networks: an overview of early research, current frameworks and new challenges. Neurocomputing 214, 242–268 (2016)CrossRefGoogle Scholar
  12. 12.
    Schikuta, E., Fuerle, T., Wanek, H.: ViPIOS: the vienna parallel input/output system. In: Pritchard, D., Reeve, J. (eds.) Euro-Par 1998. LNCS, vol. 1470, pp. 953–958. Springer, Heidelberg (1998). Scholar
  13. 13.
    Schikuta, E., Magdy, A., Mohamed, A.B.: A framework for ontology based management of neural network as a service. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9950, pp. 236–243. Springer, Cham (2016). Scholar
  14. 14.
    Schikuta, E., Mann, E.: N2Sky - neural networks as services in the clouds. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2013)Google Scholar
  15. 15.
    Schikuta, E., Weishaupl, T.: N2Grid: neural networks in the grid. In: Proceedings 2004 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 1409–1414. IEEE (2004)Google Scholar
  16. 16.
    Shen, H., Philipose, M., Agarwal, S., Wolman, A.: MCDNN: An Execution Framework for Deep Neural Networks on Resource-Constrained Devices, University of Washington and Microsoft Research (2014)Google Scholar
  17. 17.
    Zell, A., et al.: SNNS (stuttgart neural network simulator). In: Neural Network Simulation Environments, pp. 165–186. Springer, Boston (1994). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Aliaksandr Adamenko
    • 1
  • Andrii Fedorenko
    • 1
  • Erich Schikuta
    • 1
    Email author
  1. 1.University of ViennaViennaAustria

Personalised recommendations