KIPS Transactions on Computer and Communication Systems (정보처리학회논문지:컴퓨터 및 통신 시스템)

Korea Information Processing Society (한국정보처리학회)
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A Study on Mobility-Aware Edge Caching and User Association Algorithm

이동성 기반의 엣지 캐싱 및 사용자 연결 알고리즘 연구

  • Received : 2022.10.07
  • Accepted : 2022.11.03
  • Published : 2023.02.28
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Abstract

Mobile Edge Computing(MEC) is considered as a promising technology to effectively support the explosively increasing traffic demands. It can provide low-latency services and reduce network traffic by caching contents at the edge of networks such as Base Station(BS). Although users may associate with the nearest BSs, it is more beneficial to associate users to the BS where the requested content is cached to reduce content download latency. Therefore, in this paper, we propose a mobility-aware joint caching and user association algorithm to imporve the cache hit ratio. In particular, the proposed algorithm performs caching and user association based on sojourn time and content preferences. Simulation results show that the proposed scheme improves the performance in terms of cache hit ratio and latency as compared with existing schemes.

최근 스마트 디바이스 및 스트리밍 서비스의 수요 증가에 따른 네트워크 트래픽을 효과적으로 관리하기 위한 방법으로 Mobile Edge Computing(MEC)기술이 주목받고 있다. MEC는 Base Station(BS)과 같은 네트워크 엣지에 캐시를 설치함으로써 사용자에게 보다 가까운 곳에서 서비스를 제공하므로 낮은 지연시간을 제공하고, 네트워크 부하를 감소시킬 수 있다. 또한, 엣지 네트워크에서 사용자는 가장 가까운 BS와 연결되는 것보다 요청된 콘텐츠가 캐싱되어 있는 BS와 연결하는 것이 서비스 지연시간 감소에 유리하다. 따라서 본 논문에서는 캐시 적중률 향상을 위한 이동성 기반 캐싱 및 사용자 연결(user association)알고리즘을 제안한다. 제안 알고리즘은 체류시간과 콘텐츠 요청 유사도를 토대로 사용자 연결을 결정하고 콘텐츠를 캐싱한다. 시뮬레이션을 통해 기존 연구 대비 제안 알고리즘의 향상된 캐시 적중률과 감소된 지연시간을 확인한다.

Keywords

Acknowledgement

이 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구결과임(No. 2022R1A2B5B01001683).

References

  1. N. Gao, X. Xu, Y. Hou, and L. Gao, "A mobility-aware proactive caching strategy in heterogeneous ultra-dense networks," 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications(PIMRC), pp.1-7, 2019.
  2. M. K. Somesula, R. R. Rout, and D. V. L. N. Somayajulu, "Contact duration-aware cooperative cache placement using genetic algorithm for mobile edge networks," in Computer Networks, Vol.193, Article 108062, 2021.
  3. Y. Ye, M. Xiao, and M. Skoglund, "Mobility-aware content preference learning in decentralized caching networks," IEEE Transactions on Cognitive Communications and Networking, Vol.6, No.1, pp.62-73, 2020. https://doi.org/10.1109/TCCN.2019.2937519
  4. Y. Ye, M. Xiao, Z. Zhang, and Z. Ma, "Performance analysis of mobility prediction based proactive wireless caching," 2018 IEEE Wireless Communications and Networking Conference (WCNC), pp.1-6, 2018.
  5. L. E. Chatzieleftheriou, M. Karaliopoulos, and I. Koutsopoulos, "Jointly optimizing content caching and recommendations in small cell networks," IEEE Transactions on Mobile Computing, Vol.18, No.1, pp.125-138, 2019. https://doi.org/10.1109/TMC.2018.2831690
  6. G. Darzanos, L. E. Chatzieleftheriou, M. Karaliopoulos, and I. Koutsopoulos, "Content preference-aware user association and caching in cellular networks," 2020 18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT), pp.1-8, 2020.
  7. L. E. Chatzieleftheriou, G. Darzanos, M. Karaliopoulos, and I. Koutsopoulos, "Joint user association, content caching and recommendations in wireless edge networks," ACM SIGMETRICS Performance Evaluation Review, Vol.46, No.3, pp.12-17, 2019. https://doi.org/10.1145/3308897.3308905
  8. K. Ntougias, C. Psomas, E. Demarchou, I. Krikidis, and I. Koutsopoulos, "Joint dynamic wireless edge caching and user association: A stochastic optimization approach," in 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), pp.1-5, 2022.
  9. Y. Li, H. Ma, L. Wang, S. Mao, and G. Wang, "Optimized content caching and user association for edge computing in densely deployed heterogeneous networks," IEEE Transactions on Mobile Computing, Vol.21, No.6, pp. 2130-2142, June 2022. https://doi.org/10.1109/TMC.2020.3033563
  10. X. Liu, H. Zhang, K. Long, A. Nallanathan, and V. C. M. Leung, "Energy efficient user association, resource allocation and caching deployment in fog radio access networks," IEEE Transactions on Vehicular Technology, Vol.71, No.2, pp.1846-1856, 2022. https://doi.org/10.1109/TVT.2021.3131720
  11. M. Polese, R. Jana, V. Kounev, K. Zhang, S. Deb, and M. Zorzi, "Machine learning at the edge: A data-driven architecture with applications to 5G cellular networks," IEEE Transactions on Mobile Computing, Vol.20, No.12, pp.3367-3382, 2021. https://doi.org/10.1109/TMC.2020.2999852
  12. H. Farooq and A. Imran, "Spatiotemporal mobility prediction in proactive self-organizing cellular networks," IEEE Communications Letters, Vol.21, No.2, pp.370-373, 2017. https://doi.org/10.1109/LCOMM.2016.2623276
  13. H. T. Friis, "A note on a simple transmission formula," Proceedings of the IRE, Vol.34, No.5, pp.254-256, 1946. https://doi.org/10.1109/JRPROC.1946.234568
  14. J. Han, J. Pei, and M. Kamber, "Data mining: Concepts and techniques. amsterdam," The Netherlands: Elsevier, 2011.