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Analysis of time-series user request pattern dataset for MEC-based video caching scenario

MEC 기반 비디오 캐시 시나리오를 위한 시계열 사용자 요청 패턴 데이터 세트 분석

  • Akbar, Waleed (Jeju National University, Department of Computer Engineerig) ;
  • Muhammad, Afaq (Jeju National University, Department of Computer Engineerig) ;
  • Song, Wang-Cheol (Jeju National University, Department of Computer Engineerig)
  • Received : 2021.06.30
  • Accepted : 2021.08.12
  • Published : 2021.08.31

Abstract

Extensive use of social media applications and mobile devices continues to increase data traffic. Social media applications generate an endless and massive amount of multimedia traffic, specifically video traffic. Many social media platforms such as YouTube, Daily Motion, and Netflix generate endless video traffic. On these platforms, only a few popular videos are requested many times as compared to other videos. These popular videos should be cached in the user vicinity to meet continuous user demands. MEC has emerged as an essential paradigm for handling consistent user demand and caching videos in user proximity. The problem is to understand how user demand pattern varies with time. This paper analyzes three publicly available datasets, MovieLens 20M, MovieLens 100K, and The Movies Dataset, to find the user request pattern over time. We find hourly, daily, monthly, and yearly trends of all the datasets. Our resulted pattern could be used in other research while generating and analyzing the user request pattern in MEC-based video caching scenarios.

소셜 미디어 애플리케이션 및 모바일 장치의 광범위한 사용으로 인해 데이터 트래픽이 지속해서 증가하고 있다. 소셜 미디어 애플리케이션은 끝없이 많은 양의 멀티미디어 트래픽, 특히 비디오 트래픽을 생성하고 있다. YouTube, Daily Motion 및 Netflix와 같은 많은 소셜 미디어 플랫폼이 생성하는 것이다. 이러한 플랫폼에서는 다른 비디오와 비교하여 몇 개의 인기 비디오가 여러 번 요청된다. 이러한 인기 있는 비디오는 지속적인 사용자 요구 사항을 충족하기 위해 사용자 주변에 캐시해야 한다. MEC는 일관된 사용자 요구와 사용자 근접 캐시를 위한 필수 패러다임으로 부상했다. 시간에 따라 사용자 요구 패턴이 어떻게 달라지는지를 이해하는 것이 과제이다. 본 논문은 공개 데이터셋인 MovieLens 20M, MovieLens 100K, The Movies Dataset 3개를 분석하여 시간에 따른 사용자 요청 패턴을 찾는다. 모든 데이터셋의 시간별, 일별, 월별 및 연간 추세를 확인할 수 있다. MEC 기반 비디오 캐시 시나리오에서 사용자 요청 패턴을 분석 및 생성함으로써, 많은 연구에서 사용될 수 있을 것이다.

Keywords

Acknowledgement

This work was supported by the 2021 education, research and student guidance grant funded by Jeju National University.

References

  1. Danyue Wang, Xingshuo An, Xianwei Zhou, and Xing Lu. Data cache "optimization model based on cyclic genetic ant colony algorithm in edge computing environment. International Journal of Distributed Sensor Networks, 15(8):1550147719867864, 2019.
  2. Chunlin Li, Mingyang Song, Shaofeng Du, Xiaohai Wang, Min Zhang, and Youlong Luo. Adaptive priority-based cache replacement and prediction-based cache prefetching in edge computing environment. Journal of Network and Computer Applications, 165:102715, 2020. https://doi.org/10.1016/j.jnca.2020.102715
  3. Sanshan Sun, Wei Jiang, Gang Feng, Shuang Qin, and Ye Yuan. Cooperative caching with content popularity prediction for mobile edge caching. Tehnicki vjesnik, 26(2):503-509, 2019.
  4. Yayuan Tang, Kehua Guo, Jianhua Ma, Yutong Shen, and Tao Chi. A smart caching mechanism for mobile multimedia in information centric networking with edge computing. Future Generation Computer Systems, 91:590-600, 2019. https://doi.org/10.1016/j.future.2018.08.019
  5. Xing Chen, Lijun He, Shang Xu, Shibo Hu, Qingzhou Li, and Guizhong Liu. Hit ratio driven mobile edge caching scheme for video on demand services. In 2019 IEEE International Conference on Multimedia and Expo (ICME), pages 1702-1707. IEEE, 2019.
  6. Yu Chen, Yong Liu, Jingya Zhao, and Qinghua Zhu. Mobile edge cache strategy based on neural collaborative filtering. IEEE Access, 8:18475-18482, 2020. https://doi.org/10.1109/access.2020.2964711
  7. Wei Jiang, Gang Feng, Shuang Qin, and Yijing Liu. Multi-agent reinforcement learning based cooperative content caching for mobile edge networks. IEEE Access, 7:61856-61867, 2019. https://doi.org/10.1109/access.2019.2916314
  8. Jie Liang, Dali Zhu, Haitao Liu, Heng Ping, Ting Li, Hangsheng Zhang, Liru Geng, and Yinlong Liu. Multi-head attention based popularity prediction caching in social content-centric networking with mobile edge computing. IEEE Communications Letters, 2020.
  9. Sanshan Sun, Wei Jiang, Gang Feng, Shuang Qin, and Ye Yuan. Cooperative caching with content popularity prediction for mobile edge caching. Tehnicki vjesnik, 26(2):503-509, 2019.
  10. https://www.cisco.com/c/en/us/solutions/executive-perspectives/annual-internet-report/infographic-c82-741491.html
  11. Khizar Abbas, Talha Ahmed Khan, Muhammad Afaq, and Wang-Cheol Song. Network slice lifecycle management for 5g mobile networks: An intent-based networking approach. IEEE Access, 2021.
  12. ttps://www.koren.kr/eng/Network/network01.asp
  13. L. Ale, N. Zhang, H. Wu, D. Chen and T. Han, "Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network," in IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5520-5530, June 2019, doi: 10.1109/JIOT.2019.2903245.