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디바이스리스 컴퓨팅을 위한 MEC기반 대규모 엣지 디바이스 모니터링 기술 연구

MEC-Based Massive Edge Device Monitoring Techniques for Deviceless Computing

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초록

As computing technology advances, many services, including AI, that previously operated in the cloud will become usable on devices that users carry. The emergence of ultra-high-speed mobile networks like 5G dramatically increases the utility of numerous devices in the real world. In the future, with technologies like deviceless computing, the range of applications will diversify even further, and demand will continue to grow. Consequently, the importance of technology for monitoring vast amounts of device information and deploying AI services tailored to the functions and performance of each device is becoming increasingly evident. Therefore, this paper proposes a large-scale edge device monitoring technique necessary to leverage simple sensors and low-spec, low-resource devices in conjunction with Multi-access Edge Computing (MEC) to provide various AI functionalities.

Abstract

As computing technology advances, many services, including AI, that previously operated in the cloud will become usable on devices that users carry. The emergence of ultra-high-speed mobile networks like 5G dramatically increases the utility of numerous devices in the real world. In the future, with technologies like deviceless computing, the range of applications will diversify even further, and demand will continue to grow. Consequently, the importance of technology for monitoring vast amounts of device information and deploying AI services tailored to the functions and performance of each device is becoming increasingly evident. Therefore, this paper proposes a large-scale edge device monitoring technique necessary to leverage simple sensors and low-spec, low-resource devices in conjunction with Multi-access Edge Computing (MEC) to provide various AI functionalities.

참고문헌 (20)

  1. I. G. Chun, "Trends in EdgeCPS Technology for Autonomous Control of Large-Scale Devices", ETRI Electronics and Telecommunications Trends, 37(1), pp.32~41 (2022)
  2. K. J. Lee, "IoT Device Monitoring Tool based on Device Registry", Proceedings of KIIT (Korea Institute of Information Technology) Conference, pp.328~332 (2020)
  3. B. C. Kang, "K3s-Based Cluster Implementation for Edge Device Monitoring", Summer Annual Conference of IEIE (The Institute fo Electronics and Information Engineers) (2024)
  4. M. R. Islam, "Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time", Sensors, 23(11), pp.5204~ (2023)
  5. V. K. Prasad, "Efficient Resource Utilization in IoT and Cloud Computing", Information, 14(11), pp.619~ (2023)
  6. S. J. Kang, "Computing Continuum: Trends in Embedded-Edge-Cloud Computing Integration Technologies", IITP Weekly ICT Trends, (2023), pp.2~13 (2021)
  7. T. Sipola, "Artificial Intelligence in the IoT Era: A Review of Edge AI Hardware and Software", 2022 31st Conference of Open Innovations Association (FRUCT), pp.320~331 (2022)
  8. S. Dustdar, "On Distributed Computing Continuum Systems", IEEE Transactions on Knowledge and Data Engineering, 35(4), pp.4092~4105 (2022)
  9. P. K. Donta, "Exploring the Potential of Distributed Computing Continuum Systems", Computers, 12(10), pp.198~ (2023)
  10. S. Nastic, "The Essence of Software Engineering", pp.121~136 (2018), SpringerOpen
  11. J. H. Lee, "Multiaccess Edge Computing-Based Simulation as a Service for 5G Mobile Applications : A Case Study of Tollgate Selection for Autonomous Vehicles", Wireless Communications and Mobile Computing, 2020(4), pp.15~ (2020)
  12. S. H. Kim, "Local Scheduling in KubeEdge-Based Edge Computing Environment", Sensors, 23(3), pp.1522~ (2023)
  13. X. Chen, "Massive Access for 5G and Beyond", IEEE Journal on Selected Areas in Communications, 39(3), pp.615~637 (2020)
  14. H. Hua, "Edge Computing with Artificial Intelligence : A Machine Learning Perspective", ACM Computing Surveys, 55(9), pp.1~35 (2023)
  15. M. Fogli, "Performance Evaluation of Kubernetes Distributions (K8s, K3s, KubeEdge) in an Adaptive and Federated Cloud Infrastructure for Disadvantaged Tactical Networks", 2021 International Conference on Military Communication and Information Systems (ICMCIS), pp.1~7 (2021)
  16. T. Goethals, "Extending Kubernetes Clusters to Low-Resource Edge Devices Using Virtual Kubelets", IEEE Transactions on Cloud Computing, 10(4), pp.2623~2636 (2020)
  17. W. I. Seo, "SLO-Aware Inference Scheduler for Heterogeneous Processors in Edge Platforms", ACM Transactions on Architecture and Code Optimization(TACO), 18(4), pp.1~26 (2021)

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