정보처리학회논문지:소프트웨어 및 데이터공학 (KIPS Transactions on Software and Data Engineering)

  • 제11권4호
  • /
  • Pages.163-168
  • /
  • 2022
  • /
  • 2287-5905(pISSN)
  • /
  • 2734-0503(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

에지 기반 포그 컴퓨팅 환경에서 이동성 지원을 위한 라이브 마이그레이션 기반 자원 관리 기법

A Resource Management Scheme Based on Live Migrations for Mobility Support in Edge-Based Fog Computing Environments

  • 투고 : 2021.12.28
  • 심사 : 2022.01.29
  • 발행 : 2022.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

클라우드 컴퓨팅과 사물인터넷의 대중화에 따라 사물인터넷 컴퓨팅 환경에 존재하는 인터넷 연결이 가능한 장치들의 수가 점차 증가하고 있다. 또한 스마트홈, 헬스케어 등 사물인터넷을 이용한 다양한 인터넷 응용이 많아짐에 따라 통신 지연 및 연산의 신뢰성과 같은 지표의 서비스품질과 관련된 연구들이 진행되고 있다. 사물인터넷 응용의 서비스품질 향상을 위해 중앙집중형 클라우드 서버에 연결하기 보다 장치와 가까이 존재하고 중앙집중형 클라우드 서버와의 오프로드(offload) 협업을 위해 에지 컴퓨팅(edge computing)이 결함된 클라우드-포그 컴퓨팅 환경이 주목을 받고 있다. 하지만 클라우드-포그 컴퓨팅 환경에서 장치들이 이동성을 특성을 가질 때 사물인터넷 응용 서비스의 연속성이 떨어지고 서비스품질 수준이 저하되는 문제점이 발생하고 있다. 이 논문에서는 에지 기반 포그 컴퓨팅 환경에서 이동성 지원을 위한 라이브 마이그레이션 기반 자원 관리 기법을 제안한다. 제안하는 자원 관리 알고리즘은 사용자의 이동성 방향과 속도를 기반으로 일정 시간 뒤의 위치를 예측하고 이를 기반으로 라이브 마이그레이션을 통해 사물인터넷 서비스 이주를 지원한다. 성능 평가를 통해 제안하는 자원 관리 알고리즘의 효용성을 측정하였으며, 성능 실험에서 정지시간(downtime)과 서비스 작업의 신뢰성이 크게 향상됨을 보였다.

As cloud computing and the Internet of things are getting popular, the number of devices in the Internet of things computing environments is increasing. In addition, there exist various Internet-based applications, such as home automation and healthcare. In turn, existing studies explored the quality of service, such as downtime and reliability of tasks for Internet of things applications. To enhance the quality of service of Internet of things applications, cloud-fog computing (combining cloud computing and edge computing) can be used for offloading burdens from the central cloud server to edge servers. However, when devices inherit the mobility property, continuity and the quality of service of Internet of things applications can be reduced. In this paper, we propose a resource management scheme based on live migrations for mobility support in edge-based fog computing environments. The proposed resource management algorithm is based on the mobility direction and pace to predict the expected position, and migrates tasks to the target edge server. The performance results show that our proposed resource management algorithm improves the reliability of tasks and reduces downtime of services.

키워드

과제정보

이 논문은 2021학년도 평택대학교 학술연구비의 지원에 의하여 연구되었음.

참고문헌

  1. N. Zhang, C. Zhang, and D. Wu, "Construction of a smart management system for physical health based on IoT and cloud computing with big data," Computer Communications, Vol.179, pp.183-194, 2021. https://doi.org/10.1016/j.comcom.2021.08.018
  2. W. Fang, F. Xue, Y. Ding, N. Xiong, and V. C. M. Leung, "EdgeKE: An on-demand deep learning IoT system for cognitive big data on industrial edge devices," IEEE Transactions on Industrial Informatics, Vol.17, No.9, pp.6144-6152, 2021. https://doi.org/10.1109/TII.2020.3044930
  3. G. Aceto, V. Persico, and A. Pescape, "Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0," Journal of Industrial Information Integration, Vol.18, pp.100129, 2020. https://doi.org/10.1016/j.jii.2020.100129
  4. R. Zhu, S. Li, P. Wang, Y. Tan, and J. Yuan, "Gradual migration of co-existing fixed/flexible optical networks for cloud-fog computing," IEEE Access, Vol.8, pp.50637-50647, 2020. https://doi.org/10.1109/ACCESS.2020.2979895
  5. J. Feng, L. T. Yang, R. Zhang, W. Qiang, and J. Chen, "Privacy preserving high-order bi-lanczos in cloud-fog computing for industrial applications," IEEE Transactions on Industrial Informatics, pp.1-1, 2020.
  6. A. Najafizadeh, A. Salajegheh, A. M. Rahmani, and A. Sahafi, "Multi-objective task scheduling in cloud-fog computing using goal programming approach," Cluster Computing, Vol.25, No.1, pp.141-165, 2021.
  7. M. Haghi Kashani, A. M. Rahmani, and N. Jafari Navimipour, "Quality of service-aware approaches in fog computing," International Journal of Communication Systems, Vol.33, No.8, pp.e4340, 2020. https://doi.org/10.1002/dac.4340
  8. F. Murtaza, A. Akhunzada, S. U. Islam, J. Boudjadar, and R. Buyya, "QoS-aware service provisioning in fog computing," Journal of Network and Computer Applications, Vol.165, pp.102674, 2020. https://doi.org/10.1016/j.jnca.2020.102674
  9. J. C. Guevara and N. L. S. da Fonseca, "Task scheduling in cloud-fog computing systems," Peer-to-Peer Networking and Applications, Vol.14, No.2, pp.962-977, 2021. https://doi.org/10.1007/s12083-020-01051-9
  10. S. K. Mani and I. Meenakshisundaram, "Improving quality-of-service in fog computing through efficient resource allocation," Computational Intelligence, Vol.36, No.4, pp.1527-1547, 2020. https://doi.org/10.1111/coin.12285
  11. D. Goncalves, C. Puliafito, E. Mingozzi, O. Rana, L. Bittencourt, and E. Madeira, "Dynamic network slicing in fog computing for mobile users in MobFogSim," In Proceedings of the 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), Leicester, UK, 7-10 pp.237-246, Dec. 2020.
  12. R. M. Abdelmoneem, A. Benslimane, and E. Shaaban, "Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures," Computer Networks, Vol.179, pp.107348, 2020. https://doi.org/10.1016/j.comnet.2020.107348
  13. J. P. Martin, A. Kandasamy, and K. Chandrasekaran, "Mobility aware autonomic approach for the migration of application modules in fog computing environment," Journal of Ambient Intelligence and Humanized Computing, Vol.11, No.11, pp.5259-5278, 2020. https://doi.org/10.1007/s12652-020-01854-x
  14. C. Lin, G. Han, X. Qi, M. Guizani, and L. Shu, "A distributed mobile fog computing scheme for mobile delay-sensitive applications in SDN-Enabled vehicular networks," IEEE Transactions on Vehicular Technology, Vol.69, No.5, pp.5481-5493, 2020. https://doi.org/10.1109/TVT.2020.2980934
  15. V. Porkodi et al., "Resource provisioning for cyber-physical-social system in cloud-fog-edge computing using optimal flower pollination algorithm," IEEE Access, Vol.8, pp.105311-105319, 2020. https://doi.org/10.1109/ACCESS.2020.2999734
  16. M. S. Aslanpour, S. S. Gill, and A. N. Toosi, "Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research," Internet of Things, Vol.12, pp.100273, 2020. https://doi.org/10.1016/j.iot.2020.100273
  17. Y. Kalyani and R. Collier, "A systematic survey on the role of cloud, fog, and edge computing combination in smart agriculture," Sensors, Vol.21, No.17, pp.5922, 2021. https://doi.org/10.3390/s21175922
  18. A. Oliveira and T. Vazao, "Generating synthetic datasets for mobile wireless networks with SUMO," in Proceedings of the 19th ACM International Symposium on Mobility Management and Wireless Access, Alicante, Spain, pp.33-42, 2021.
  19. J. J. Gonzalez-Delicado, J. Gozalvez, J. Mena-Oreja, M. Sepulcre, and B. Coll-Perales, "Alicante-murcia freeway scenario: A high-accuracy and large-scale traffic simulation scenario generated using a novel traffic demand calibration method in SUMO," IEEE Access, Vol.9, pp.154423-154434, 2021. https://doi.org/10.1109/ACCESS.2021.3126269