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Partial Offloading System of Multi-branch Structures in Fog/Edge Computing Environment

FEC 환경에서 다중 분기구조의 부분 오프로딩 시스템

  • Lee, YonSik (School of Computer Info. & Comm., Kunsan National University) ;
  • Ding, Wei (Shandong Computer Science Center, Qilu University of Technology) ;
  • Nam, KwangWoo (School of Computer Info. & Comm., Kunsan National University) ;
  • Jang, MinSeok (School of Computer Info. & Comm., Kunsan National University)
  • Received : 2022.09.12
  • Accepted : 2022.09.22
  • Published : 2022.10.31

Abstract

We propose a two-tier cooperative computing system comprised of a mobile device and an edge server for partial offloading of multi-branch structures in Fog/Edge Computing environments in this paper. The proposed system includes an algorithm for splitting up application service processing by using reconstructive linearization techniques for multi-branch structures, as well as an optimal collaboration algorithm based on partial offloading between mobile device and edge server. Furthermore, we formulate computation offloading and CNN layer scheduling as latency minimization problems and simulate the effectiveness of the proposed system. As a result of the experiment, the proposed algorithm is suitable for both DAG and chain topology, adapts well to different network conditions, and provides efficient task processing strategies and processing time when compared to local or edge-only executions. Furthermore, the proposed system can be used to conduct research on the optimization of the model for the optimal execution of application services on mobile devices and the efficient distribution of edge resource workloads.

본 논문에서는 FEC (Fog/Edge Computing) 환경에서 다중 분기구조의 부분 오프로딩을 위해 모바일 장치와 에지서버로 구성된 2계층 협력 컴퓨팅 시스템을 제안한다. 제안 시스템은 다중 분기구조에 대한 재구성 선형화 기법을 적용하여 응용 서비스 처리를 분할하는 알고리즘과 모바일 장치와 에지 서버 간의 부분 오프로딩을 통한 최적의 협업 알고리즘을 포함한다. 또한 계산 오프로딩 및 CNN 계층 스케줄링을 지연시간 최소화 문제로 공식화하고 시뮬레이션을 통해 제안 시스템의 효과를 분석한다. 실험 결과 제안 알고리즘은 DAG 및 체인 토폴로지 모두에 적합하고 다양한 네트워크 조건에 잘 적응할 수 있으며, 로컬이나 에지 전용 실행과 비교하여 효율적인 작업 처리 전략 및 처리시간을 제공한다. 또한 제안 시스템은 모바일 장치에서의 응용 서비스 최적 실행을 위한 모델의 경량화 및 에지 리소스 워크로드의 효율적 분배 관련 연구에 적용 가능하다.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1F1A1047768) and a grant (22RITD-C161698-02) from Regional Innovation Technology Development Program funded by Ministry of Land, Infrastructure and Transport of Korean government. This work was supported in part by the "Belt and Road" Innovative Talent Exchange Foreign Expert Project of the Ministry of Science and Technology of China, No. DL2022024004L.

References

  1. Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, "A Survey on Mobile Edge Computing: The Communication Perspective," IEEE Communication Survey Tutorial, vol. 19, no. 4, pp. 2322-2358, Aug. 2017. https://doi.org/10.1109/COMST.2017.2745201
  2. L. Lin, X. Liao, H. Jin, and P. Li, "Computation Offloading Toward Edge Computing," in Proceedings of IEEE 2019, vol. 107, no. 8, pp. 1584-1607, 2019.
  3. Z. Ning, P. Dong, X. Kong, and F. Xia, "A Cooperative Partial Computation Offloading Scheme for Mobile Edge Computing Enabled Internet of Things," IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4804-4814, Jun. 2019. https://doi.org/10.1109/JIOT.2018.2868616
  4. Y. Lee, K. Nam, and M. Jang, "Extracting optimal moving patterns of edge devices for efficient resource placement in an FEC environment," Journal of the Korea Institute of Information and Communication Engineering, vol. 26, no. 1, pp. 162-169, Jan. 2022. https://doi.org/10.6109/JKIICE.2022.26.1.162
  5. Z. Kuang, L. Li, J. Gao, L. Zhao, and A. Liu, "Partial offloading scheduling and power allocation for mobile edge computing systems," IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6774-6785, Aug. 2019. https://doi.org/10.1109/JIOT.2019.2911455
  6. S. E. Mahmoodi, R. N. Uma, and K. P. Subbalakshmi, "Optimal Joint Scheduling and Cloud Offloading for Mobile Applications," IEEE Transaction on Cloud Computing, vol. 7, no. 2, pp. 301-313, Apr. 2019. https://doi.org/10.1109/TCC.2016.2560808
  7. J. Ren, G. Yu, Y. Cai, and Y. He, "Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading," IEEE Transaction on Wireless Communication, vol. 17, no. 8, pp. 5506-5519, Aug. 2018. https://doi.org/10.1109/TWC.2018.2845360
  8. H. H. Harvey, Y. Mao, Y. Hou, and B. Sheng, "EDOS: Edge Assisted Offloading System for Mobile Devices," in Proceedings of 26th International Conference on Computer Communication Network, Vancouver: BC, Canada, pp. 1-9, 2017.
  9. H. Guo, J. Liu, J. Zhang, W. Sun, and N. Kato, "MobileEdge Computation Offloading for Ultradense IoT Networks," IEEE Internet Things Journal, vol. 5, no. 6, pp. 4977-4988, Dec. 2018. https://doi.org/10.1109/JIOT.2018.2838584
  10. W. Chen, D. Wang, and K. Li, "Multi-User Multi-Task Computation Offloading in Green Mobile Edge Cloud Computing," IEEE Transaction on Services Computing, vol. 12, no. 5, pp. 726-738, Sep.-Oct. 2019. https://doi.org/10.1109/TSC.2018.2826544
  11. X. Tian, J. Zhu, T. Xu, and Y. Li, "Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds," Sensors, vol. 21, no. 1, pp. 229, Jan. 2021. https://doi.org/10.3390/s21010229
  12. S. Yu, X. Wang, and R. Langar, "Computation offloading for mobile edge computing: A deep learning approach," in Proceedings of IEEE 28th Annual International Symposium on Indoor Mobile Radio Communication, Montreal: QC, Canada, pp. 1-6, 2017.
  13. T. Q. Dinh, J. Tang, Q. D. La, and Q. S. Quek, "Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling," IEEE Transaction on Communication, vol. 65, no. 8, pp. 3571-3584, Aug. 2017.
  14. J. Du, L. Zhao, J. Feng, and X. Chu, "Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems with Min-Max Fairness Guarantee," IEEE Transaction on Communication, vol. 66, no. 4, pp. 1594-1608, Apr. 2018. https://doi.org/10.1109/TCOMM.2017.2787700
  15. H. Jeong, H. Lee, C. Shin, S. Moon, "IONN: Incremental Offloading of Neural Network Computations from Mobile Devices to Edge Servers," in Proceedings of the ACM Symposium on Cloud Computing, New York: NY, USA, pp. 401-411, 2018.