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A Cloud Computing Based Supporting Technology for the Lightweight Application Service

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2020 International Conference on Applications and Techniques in Cyber Intelligence (ATCI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1244))

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Abstract

With the continuous advancement of dispatching and control cloud, rich applications are integrated on this platform. However, the overall resource utilization rate of the platform is low. How to use the platform resources reasonably and efficiently becomes a major difficulty in the process of dispatching and control cloud’s continuous promotion. The cloud platform needs to study the technology of lightweight application service life cycle management with the goal of service lightweight and resource optimal allocation, ensure the safe and stable operation of the cloud platform. In this paper, the container cluster management technology is used to realize the application service lightweight of the regulatory cloud platform and improve the ability of the large-scale resource optimal allocation of the cloud platform by automatic arrangement technology of container.

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References

  1. Xu, H.: The architecture of dispatching and control cloud and its application prospect. Power Syst. Technol. 41(10), 3104–3111 (2017). (in Chinese)

    Google Scholar 

  2. Zhang, J.: The solution of data synchronization and acquisition for power grid reserved dispatching system. Power Syst. Commun. 30(8), 47–50 (2009)

    Google Scholar 

  3. Xu, C., Yu, J.: The regional dispatching and control technology support system based on cloud technology. Electr. Power Eng. Technol. 34(3), 5–9 (2015). (in Chinese)

    Google Scholar 

  4. Li, D., Chen, Z., Deng, Z., et al.: A wide area service oriented architecture design for plug and play of power grid equipment. Procedia Comput. Sci. 129, 353–357 (2018)

    Article  Google Scholar 

  5. Chen, Z., Li, D., Deng, Z., et al.: The application of power grid equipment plug and play based on wide area SOA. In: Proceedings of 2nd IEEE International Conference on Energy Internet, Beijing, pp. 19–23. IEEE (2018)

    Google Scholar 

  6. Chen, Z., Chen, Y., Gao, X., et al.: Unobtrusive sensing incremental social contexts using fuzzy class incremental learning. In: Proceedings of International Conference on Data Mining, USA, pp. 71–80. IEEE (2015)

    Google Scholar 

  7. Walraven, S., Truyen, E., Joosen, W.: Comparing PaaS offerings in light of SaaS development. Computing 96(8), 669–724 (2014)

    Article  Google Scholar 

  8. Boettiger, C.: An introduction to Docker for reproducible research. ACM SIGOPS Oper. Syst. Rev. 49(1), 71–79 (2015)

    Article  Google Scholar 

  9. Gao, X., Hoi, S.C., Zhang, Y., et al.: SOML: sparse online metric learning with application to image retrieval. In: Proceedings of AAAI, USA, pp. 1206–1212 (2014)

    Google Scholar 

  10. Saito, H., Lee, H.C.C., Hsu, K.J.C.: Kubernetes Cookbook. Packt Publishing Ltd, Birmingham (2016)

    Google Scholar 

  11. Xiang, Z., Chen, Z., Gao, X., et al.: Solving large-scale tsp using a fast wedging insertion partitioning approach. Math. Probl. Eng. 2015, 1–9 (2015)

    MathSciNet  MATH  Google Scholar 

  12. Geng, X., Zeng, X., Hu, L., et al.: An novel architecture and inter-process communication scheme to adapt chromium based on docker container. Procedia Comput. Sci. 107(C), 691–696 (2017)

    Article  Google Scholar 

  13. Chen, Z., Chen, Y., Hu, L., et al.: ContextSense: unobtrusive discovery of incremental social context using dynamic Bluetooth data. In: Proceedings of the 2014 ACM Conference on Pervasive and Ubiquitous Computing, pp. 23–26. ACM (2014)

    Google Scholar 

  14. Chen, Z., Wang, S., Shen, Z., et al.: Online sequential ELM based transfer learning for transportation mode recognition. In: Proceedings of the 6th IEEE International Conference on Cybernetics and Intelligent Systems, pp. 78–83. ACM (2014)

    Google Scholar 

  15. Li, J., Luo, Y., Lang, Y., et al.: Integrated node-branch computing model service of large power grid for unified analysis.Power Syst. Technol. 41(5), 1468–1475 (2017). (in Chinese)

    Google Scholar 

  16. Ji, Z.-x., Li, L.-x., Yuan, R.-c., Huang, Y., Di, F.-c., Chen, L.: The design and implementation of virtualization technology application on power grid dispatching automation system. In: 2013 3rd International Conference on Electrical Engineering and Automatic Control, Jinan, China (2013)

    Google Scholar 

  17. Chen, Z., Li, D., Huang, Y., et al.: A partition coordinated and optimized operation design for power grid wide area coordination and interaction service. In: Proceedings of 2nd IEEE Conference on Energy Internet and Energy System Integration, Beijing. IEEE (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate School of Chinese Academy of Social Sciences, Beijing, 102488, China

    Kelong Wang

Authors
  1. Kelong Wang
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Corresponding author

Correspondence to Kelong Wang .

Editor information

Editors and Affiliations

  1. Distributed System and Security Research Cluster, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. Shanghai University of Medicine and Health Sciences, Shanghai, China

    Zheng Xu

  4. University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

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Wang, K. (2021). A Cloud Computing Based Supporting Technology for the Lightweight Application Service. In: Abawajy, J., Choo, KK., Xu, Z., Atiquzzaman, M. (eds) 2020 International Conference on Applications and Techniques in Cyber Intelligence. ATCI 2020. Advances in Intelligent Systems and Computing, vol 1244. Springer, Cham. https://doi.org/10.1007/978-3-030-53980-1_39

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