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Delayed offloading scheme for IoT tasks considering opportunistic fog computing environment

기회적 포그 컴퓨팅 환경을 고려한 IoT 테스크의 지연된 오프로딩 제공 방안

  • 경연웅 (한신대학교 컴퓨터공학부)
  • Received : 2020.11.14
  • Accepted : 2020.12.18
  • Published : 2020.12.31

Abstract

According to the various IoT(Internet of Things) services, there have been lots of task offloading researches for IoT devices. Since there are service response delay and core network load issues in conventional cloud computing based offloadings, fog computing based offloading has been focused whose location is close to the IoT devices. However, even in the fog computing architecture, the load can be concentrated on the for computing node when the number of requests increase. To solve this problem, the opportunistic fog computing concept which offloads task to available computing resources such as cars and drones is introduced. In previous fog and opportunistic fog node researches, the offloading is performed immediately whenever the service request occurs. This means that the service requests can be offloaded to the opportunistic fog nodes only while they are available. However, if the service response delay requirement is satisfied, there is no need to offload the request immediately. In addition, the load can be distributed by making the best use of the opportunistic fog nodes. Therefore, this paper proposes a delayed offloading scheme to satisfy the response delay requirements and offload the request to the opportunistic fog nodes as efficiently as possible.

다양한 IoT(Internet of Things) 서비스들이 등장하면서 IoT 기기의 테스크를 오프로딩 시키는 연구가 진행되었다. 기존에는 클라우드 컴퓨팅을 통한 오프로딩이 고려되었지만 서비스 응답 지연 및 코어 네트워크의 부하 등의 이슈로 인해 IoT 기기 근처에서 오프로딩을 지원하는 포그 컴퓨팅 개념이 도입되었다. 하지만 포그 컴퓨팅 환경에서도 서비스 대상 IoT 기기가 증가하게 되면 클라우드 환경과 마찬가지로 부하 집중 문제로 인해 서비스 응답 지연이 발생할 수 있다. 이를 해결하기 위하여 자동차, 드론 등 IoT 기기 근처에 존재하는 컴퓨팅 가능 노드들을 통해 오프로딩을 수행하는 개념인 기회적 포그 컴퓨팅이 등장하였다. 기존의 포그 및 기회적 포그 컴퓨팅 노드들을 활용한 오프로딩 연구들은 서비스의 요청이 있을 때 가용한 노드를 통해 오프로딩을 수행한다. 기존의 연구 방법대로 오프로딩을 수행한다면 기회적 포그 컴퓨팅 노드가 가용할 때에 발생된 요청들만 해당 노드들로 오프로딩이 가능하다. 하지만 서비스의 응답 지연 요구사항만 만족시킨다면 즉시적으로 요청을 처리할 필요가 없고 최대한 많은 테스크를 기회적 포그 컴퓨팅 노드로 오프로딩 시키는 것이 부하 분산에 용이하다. 그러므로 본 논문에서는 오프로딩 타이머를 기반으로 서비스 응답 지연 요구사항을 만족시키면서 최대한 기회적 포그 컴퓨팅 노드들을 통해 오프로딩 시킬 수 있는 지연된 오프로딩 방법을 제안하고자 한다.

Keywords

References

  1. D.W.Lee, K.Cho, and S.H.Lee, "Analysis on Smart Factory in IoT Environment," Journal of The Korea Internet of Things Society, Vol.5, No.2, pp.1-5, 2019. https://doi.org/10.1016/j.iot.2018.11.001
  2. K.B.Jan,g, "A study on IoT platform for private electrical facilities management," Journal of The Korea Internet of Things Society, Vol.5, No.2, pp.103-110, 2019. https://doi.org/10.20465/KIOTS.2019.5.2.103
  3. Y.W.Kyung and T.K.Kim, "Flow Handover Management Scheme based on QoS in SDN Considering IoT," Journal of The Korea Internet of Things Society, Vol.6, No.2, pp.45-50 2020 https://doi.org/10.20465/KIOTS.2020.6.2.045
  4. P.Mach and Z.Becvar, "Mobile Edge Computing: A Survey on Architecture and Computation Offloading," IEEE Communications Surveys & Tutorials, Vol.19, No.3, pp.1628-1656, 2017. https://doi.org/10.1109/COMST.2017.2682318
  5. M.Mukherjee, S.Kumar, C.X.Mavromoustakis, G.Mastorakis, R.Matam, V.Kumar, and Q.Zhang, "Latency-driven Parallel Task Data Offloading in Fog Computing Networks for Industrial Applications," IEEE Transactions on Industrial Informatics, Vol.16, No.9, pp.6050-6058, 2020. https://doi.org/10.1109/TII.2019.2957129
  6. Y.Jiang and D.H.K.Tsang, "Delay-Aware Task Offloading in Shared Fog Networks," IEEE Internet of Things Journal, Vol.5, No.6, pp.4945-4956, 2018. https://doi.org/10.1109/JIOT.2018.2880250
  7. A.Yousefpour, G.Ishigaki, R.Gour, and J.P.Jue, "On Reducing IoT Service Delay via Fog Offloading," IEEE Internet of Things Journal, Vol.5, No.2, pp.998-1010, 2018. https://doi.org/10.1109/JIOT.2017.2788802
  8. J.Ren, G.Yu, Y.He, and G.Y.Li, "Collaborative Cloud and Edge Computing for Latency Minimization," IEEE Transactions on Vehicular Technology, Vol.68, No.5, pp.5031-5044, 2019. https://doi.org/10.1109/TVT.2019.2904244
  9. N.Fernando, S.W.Loke, I.Avazpour, F.Chen, A.B.Abkenar, and A.Ibrahim, "Opportunistic Fog for IoT: Challenges and Opportunities," IEEE Internet of Things Journal, Vol.6, No.5, pp.8897-8910, 2019. https://doi.org/10.1109/JIOT.2019.2924182
  10. Y.Liu, S.Wang, Q.Zhao, S.Du, A.Zhou, X.Ma, and F.Yang, "Dependency-Aware Task Scheduling in Vehicular Edge Computing," IEEE Internet of Things Journal, Vol.7, No.6, pp.4961-4971, 2020. https://doi.org/10.1109/JIOT.2020.2972041
  11. Z.Ning, J.Juang, X.Wang, J.J.P.C.Rodrigues, and L.Guo, "Mobile Edge Computing-Enabled Internet of Vehicles: Toward Energy-Efficient Scheduling," IEEE Network, Vol.33, No.5, pp.198-205, 2019. https://doi.org/10.1109/MNET.2019.1800309
  12. X.Wang, Z.Ning, and L.Wang, "Offloading in Internet of Vehicles: A Fog-enabled Real-time Traffic Management System," IEEE Transactions on Industrial Informatics" Vol.14, No.10, pp.4568-4578, 2018. https://doi.org/10.1109/TII.2018.2816590
  13. Z.Ning, P.Dong, X.Wang, J.J.P.C.Rodrigues, and F.Xia, "Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System," ACM Transactions on Intelligent Systems and Technology, Vol.10, No.6, pp.1-24, 2019.
  14. M.Li, P.Si, and Y.Zhang, "Delay-Tolerant Data Traffic to Software-Defined Vehicular Networks with Mobile Edge Computing in Smart City," IEEE Transactions on Vehicular Technology, Vol.67, No.10, pp.9073-9086, 2018. https://doi.org/10.1109/TVT.2018.2865211
  15. Y.Liu, W.Wang, Y.Ma, Z.Yang, and F.Yu, "Distributed Task Offloading in Heterogeneous Vehicular Crowd Sensing," MDPI Sensors, Vo.16, No.7, 2016.
  16. J.Lee, G.Lee, and S.Pack, "Pseudonyms in IPv6 ITS Communications: Use of Pseudonyms, Performance Degradation, and Optimal Pseudonyms Change," International Journal of Distributed Sensor Networks, Vol.11, No.5, pp.1-7, 2015.
  17. Q.Fan and N.Ansari, "Towards Workload Balancing in Fog Computing Empowered IoT," IEEE Transactions on Network Service and Engineering, Vol.7, No.1, pp.253-262, 2018.