DOI QR코드

DOI QR Code

Design of Efficient Edge Computing based on Learning Factors Sharing with Cloud in a Smart Factory Domain

스마트 팩토리 환경에서 클라우드와 학습된 요소 공유 방법 기반의 효율적 엣지 컴퓨팅 설계

  • Hwang, Zi-on (Department of Smart Systems Software Engineering, Hyupsung University)
  • Received : 2017.09.08
  • Accepted : 2017.09.21
  • Published : 2017.11.30

Abstract

In recent years, an IoT is dramatically developing according to the enhancement of AI, the increase of connected devices, and the high-performance cloud systems. Huge data produced by many devices and sensors is expanding the scope of services, such as an intelligent diagnostics, a recommendation service, as well as a smart monitoring service. The studies of edge computing are limited as a role of small server system with high quality HW resources. However, there are specialized requirements in a smart factory domain needed edge computing. The edges are needed to pre-process containing tiny filtering, pre-formatting, as well as merging of group contexts and manage the regional rules. So, in this paper, we extract the features and requirements in a scope of efficiency and robustness. Our edge offers to decrease a network resource consumption and update rules and learning models. Moreover, we propose architecture of edge computing based on learning factors sharing with a cloud system in a smart factory.

최근 사물인터넷은 인공지능의 발전, 연결된 기기의 증가와 클라우드 시스템의 높은 성능으로 인해 급격하게 발전하고 있다. 많은 기기와 센서로부터 생산되는 엄청난 양의 데이터들은 지능적 진단, 추천 서비스 뿐 아니라 스마트 관제 서비스와 같이 서비스 영역의 확대를 이끌고 있다. 엣지 컴퓨팅(Edge Computing)에 대한 연구는 높은 성능을 지닌 하드웨어를 바탕으로 작은 또 하나의 서버로써의 역할에 국한되어 연구되고 있다. 그러나 데이터를 분석하고 의미성에 따른 서비스를 구현하기 위해서는 범용적 서버로써의 역할보다는 도메인에 특화된 기능과 요구사항을 지녀야 한다. 스마트 팩토리에서의 엣지는 제한적 필터링, 사전 포맷팅을 포함하는 전처리와 그룹 컨텍스트 융합, 지역적 룰의 관리 등을 필요로 한다. 따라서 본 논문에서는 공장 특성에 맞는 효율성과 강건함 측면을 강조하는 요구사항들을 도출하고, 클라우드와 학습된 요소 공유 방법을 기반으로 하는 엣지 컴퓨팅의 구조를 제안하고자 한다. 이 엣지는 네트워크 자원 소모를 감소시키고 룰과 학습화된 모델의 변경을 쉽게 할 수 있도록 한다.

Keywords

References

  1. P. G. Lopez, A. Montresor, D. Epema, et al., "Edge-centric Computing: Vision and Challenges," ACM SIGCOMM Computer Communication Review, vol. 45, no. 5, pp. 37-42, Oct. 2015. https://doi.org/10.1145/2831347.2831354
  2. W. Shi, J. Cao, Q. Zhang, et al., "Edge Computing: Vision and Challenges," IEEE Internet of Things Journal, vol. 3, no. 5, Oct. 2016.
  3. F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "Fog Computing and its role in the Internet of Things," in Proc. 1st Edition MCC Workshop Mobile Cloud Computing, pp. 13-16, Aug. 2012.
  4. C. C. Byers, "Architectural Imperatives for Fog Computing: Use Cases, Requirements, and Architectural Techniques for For-Enabled IoT Networks," IEEE Communications Magazine, vol. 55, no. 8, pp. 14-20, Aug. 2017. https://doi.org/10.1109/MCOM.2017.1600885
  5. X. Sun, N. Ansari, "EdgeIoT: Mobile Edge Computing for the Internet of Things," IEEE Communications Magazine, vol. 54, no. 12, pp. 22-29, Dec. 2016. https://doi.org/10.1109/MCOM.2016.1600492CM
  6. K. Dolui, and S. K. Datta, "Comparison of Edge Computing Implementations: For Computing, Cloudlet and Mobile Edge Computing," Global Internet of Things Summit (GIoTS), Jun. 2017.
  7. T. Yaofeng, D. Zhenjiang, Y. Hongzhang, "Key Technologies and Application of Edge Computing," ZTE Communications, vol. 15, no. 2, pp. 26-34, Apr. 2017.
  8. A. Houmandadr, S. A. Zonouz, and R. Berthier, "A Cloud-based Intrusion Detection and Response System for Mobile Phones," IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, pp. 31-32, Jun. 2011.
  9. A. Beloglazov, J. Abawajy, and R. Buyya, "Energy-aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing," Future Generation Computer Systems, vol. 28, no. 5, pp. 755-768, May 2012. https://doi.org/10.1016/j.future.2011.04.017
  10. S. Yang, "IoT Stream Processing and Analytics in the Fsog," IEEE Communications Magazine, vol. 55, no. 8, pp. 21-27, Aug. 2017. https://doi.org/10.1109/MCOM.2017.1600840
  11. M. Lee, Y. Uhm, Y. Kim, et al., "Intelligent Power Management Device With Middleware Based Living Pattern Learning for Power Reduction," IEEE Transactions on Consumer Electronics, vol. 55, no. 4, pp. 2081-2089, Nov. 2009. https://doi.org/10.1109/TCE.2009.5373772
  12. J. C. Na, G. P. Kumar, "Quality of Service in Meta Cloud," Asia-pacific Journal of Convergent Research Interchange, vol.1, no.3, pp. 53-57, September 2015.
  13. J. P. Hong, E. J. Kim, and H. Y. Park, "An analysis of determinants for artificial intelligence industry competitiveness," Journal of the Korea Institute of Information and Communication Engineering, vol.21, no.4, pp.663-671, Apr. 2017. https://doi.org/10.6109/JKIICE.2017.21.4.663