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CoPUP: content popularity and user preferences aware content caching framework in mobile edge computing

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Abstract

Mobile edge computing (MEC) enables intelligent content caching at the network edge to reduce traffic and enhance content delivery efficiency. In MEC architecture, popular content can be deployed at the MEC server to improve users’ quality of experience (QoE). Existing content caching techniques attempt to improve cache hits but do not consider users’ preferences while estimating the popularity of content. Knowing users’ preferences is beneficial and essential for efficient content caching. This paper proposes Content Popularity and User Preferences aware content caching (CoPUP) in MEC. The proposed scheme uses content-based collaborative filtering first to analyze the user-content matrix and identify the relationships between different contents. The convolution neural network model (CNN) is used to predict users’ preferences. The CoPUP significantly improves cache performance, enhances cache hit ratio, and reduces response time. The simulation experiments are conducted based on the real dataset from Movielens. The proposed CoPUP technique is compared with three baseline techniques namely Least Frequently Used (LFU), Least Recently Used (LRU), First-In-First-Out (FIFO) and a state-of-the-art technique Mobility-Aware Proactive edge caching scheme based on federated learning (MPCF). The experimental results reveal that the proposed model achieves 2% higher cache hit ratio and faster response time compared with baseline and state-of-the-art techniques.

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References

  1. Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., Sabella, D.: On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Commun. Surv. Tutorials 19, 1657–1681 (2017)

    Article  Google Scholar 

  2. Zaman, S.K.u., Jehangiri, A.I., Maqsood, T., Umar, A.I., Khan, M.A., Jhanjhi, N.Z., et al., “COME-UP: Computation Offloading in Mobile Edge Computing with LSTM Based User Direction Prediction,“ Applied Sciences, vol. 12, p. 3312, 2022

  3. uz Zaman, S.K., Tahir, M.A., Maqsood, Bilal, K.: A load balanced task scheduling heuristic for large-scale computing systems. Comput. Syst. Sci. Eng. 34, 4 (2019)

    Google Scholar 

  4. Safavat, S., Sapavath, N.N., Rawat, D.B.: Recent advances in mobile edge computing and content caching. Digit. Commun. Networks 6, 189–194 (2020)

    Article  Google Scholar 

  5. uz Zaman, S.K., Jehangiri, A.I., Maqsood, T., Ahmad, Z., Umar, A.I., Shuja, J., et al., “Mobility-aware computational offloading in mobile edge networks: a survey,“ Cluster Computing, pp. 1–22, 2021

  6. Zhou, S., Jadoon, W., Shuja, J., “Machine learning-based offloading strategy for lightweight user mobile edge computing tasks,“ Complexity, vol. 2021, 2021

  7. Shuja, J., Bilal, K., Alasmary, W., Sinky, H., Alanazi, E.: Applying machine learning techniques for caching in next-generation edge networks: A comprehensive survey. J. Netw. Comput. Appl. 181, 103005 (2021)

    Article  Google Scholar 

  8. Patel, M., Naughton, B., Chan, C., Sprecher, N., Abeta, S., Neal, A., “Mobile-edge computing introductory technical white paper,“ White paper, mobile-edge computing (MEC) industry initiative, vol. 29, pp. 854–864, 2014

  9. Jehangiri, A.I., Maqsood, T., Umar, A.I., Shuja, J., Ahmad, Z., Dhaou, I.B., et al., “LiMPO: lightweight mobility prediction and offloading framework using machine learning for mobile edge computing” Cluster Computing, pp. 1–19, 2022

  10. Goian, H.S., Al-Jarrah, O.Y., Muhaidat, S., Al-Hammadi, Y., Yoo, P., Dianati, M.: Popularity-based video caching techniques for cache-enabled networks: a survey. IEEE Access. 7, 27699–27719 (2019)

    Article  Google Scholar 

  11. Li, C., Song, M., Yu, C., Luo, Y.: “Mobility and marginal gain based content caching and placement for cooperative edge-cloud computing. Inf. Sci. 548, 153–176 (2021)

    Article  Google Scholar 

  12. Shuja, J., Mustafa, S., Ahmad, R.W., Madani, S.A., Gani, A., Khan, M.K.: Analysis of vector code offloading framework in heterogeneous cloud and edge architectures. IEEE Access. 5, 24542–24554 (2017)

    Article  Google Scholar 

  13. Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: “A survey on mobile edge computing: The communication perspective”. IEEE Commun. Surv. Tutorials 19, 2322–2358 (2017)

    Article  Google Scholar 

  14. Ahmad, Z., Jehangiri, A.I., Ala’anzy, M.A., Othman, M., Latip, R., Zaman, S.K.U., et al.: “Scientific Workflows Management and Scheduling in Cloud Computing: Taxonomy, Prospects, and Challenges”. IEEE Access. 9, 53491–53508 (2021)

    Article  Google Scholar 

  15. Shuja, J., Bilal, K., Alasmary, W., Sinky, H., Alanazi, E.: “Applying machine learning techniques for caching in next-generation edge networks: A comprehensive survey” Journal of Network and Computer Applications, p. 103005, 2021

  16. Elgendy, I.A., Zhang, W., Tian, Y.-C., Li, K.: Resource allocation and computation offloading with data security for mobile edge computing. Future Generation Computer Systems 100, 531–541 (2019)

    Article  Google Scholar 

  17. Park, S., Oh, S., Nam, Y., Bang, J., Lee, E., “Mobility-aware distributed proactive caching in content-centric vehicular networks,“ in 2019 12th IFIP Wireless and Mobile Networking Conference (WMNC), 2019, pp. 175–180

  18. Wei, H., Luo, H., Sun, Y., “Mobility-Aware Service Caching in Mobile Edge Computing for Internet of Things,“ Sensors, vol. 20, p. 610, 2020

  19. Zhang, K., Leng, S., He, Y., Maharjan, S., Zhang, Y.: Cooperative content caching in 5G networks with mobile edge computing. IEEE Wirel. Commun. 25, 80–87 (2018)

    Article  Google Scholar 

  20. Yao, L., Chen, A., Deng, J., Wang, J., Wu, G.: “A cooperative caching scheme based on mobility prediction in vehicular content centric networks”. IEEE Trans. Veh. Technol. 67, 5435–5444 (2017)

    Article  Google Scholar 

  21. Fang, S., Fan, P., “A cooperative caching algorithm for cluster-based vehicular content networks with vehicular caches,“ in: 2017 IEEE Globecom Workshops (GC Wkshps), 2017, pp. 1–6

  22. Li, C., Zhang, Y., Song, M., Yan, X., Luo, Y.: “An optimized content caching strategy for video stream in edge-cloud environment” Journal of Network and Computer Applications, p. 103158, 2021

  23. Su, Z., Hui, Y., Xu, Q., Yang, T., Liu, J., Jia, Y.: An edge caching scheme to distribute content in vehicular networks. IEEE Trans. Veh. Technol. 67, 5346–5356 (2018)

    Article  Google Scholar 

  24. Mahmood, A., Casetti, C.E., Chiasserini, C.F., Giaccone, P., Härri, J.: The rich prefetching in edge caches for in-order delivery to connected cars. IEEE Trans. Veh. Technol. 68, 4–18 (2018)

    Article  Google Scholar 

  25. Jiang, W., Feng, G., Qin, S., Liang, Y.-C., “Learning-based cooperative content caching policy for mobile edge computing,“ in ICC 2019–2019 IEEE International Conference on Communications (ICC), 2019, pp. 1–6

  26. Jiang, Y., Ma, M., Bennis, M., Zheng, F.-C., You, X.: User preference learning-based edge caching for fog radio access network. IEEE Trans. Commun. 67, 1268–1283 (2018)

    Article  Google Scholar 

  27. Yu, Z., Hu, J., Min, G., Zhao, Z., Miao, W., Hossain, M.S.: Mobility-aware proactive edge caching for connected vehicles using federated learning. IEEE Trans. Intell. Transp. Syst. 22, 5341–5351 (2020)

    Article  Google Scholar 

  28. Abousaleh, F.S., Cheng, W.-H., Yu, N.-H., Tsao, Y.: Multimodal deep learning framework for image popularity prediction on social media. IEEE Trans. Cogn. Dev. Syst. 13, 679–692 (2020)

    Article  Google Scholar 

  29. Sarwar, B., Karypis, G., Konstan, J., Riedl, J., “Item-based collaborative filtering recommendation algorithms,“ in Proceedings of the 10th international conference on World Wide Web, 2001, pp. 285–295

  30. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S., “Neural collaborative filtering,“ in Proceedings of the 26th international conference on world wide web, 2017, pp. 173–182

  31. Xue, F., He, X., Wang, X., Xu, J., Liu, K., Hong, R.: Deep item-based collaborative filtering for top-n recommendation. ACM Trans. Inform. Syst. (TOIS) 37, 1–25 (2019)

    Article  Google Scholar 

  32. Ale, L., Zhang, N., Wu, H., Chen, D., Han, T.: Online proactive caching in mobile edge computing using bidirectional deep recurrent neural network. IEEE Internet of Things Journal 6, 5520–5530 (2019)

    Article  Google Scholar 

  33. Harper, F.M., Konstan, J.A.: The movielens datasets: History and context. Acm Trans. Interact. Intell. Syst. (tiis) 5, 1–19 (2015)

    Google Scholar 

  34. Müller, S., Atan, O., van der Schaar, M., Klein, A.: Context-aware proactive content caching with service differentiation in wireless networks. IEEE Trans. Wireless Commun. 16, 1024–1036 (2016)

    Article  Google Scholar 

  35. Li, S., Xu, J., van der Schaar, M., Li, W.: Trend-aware video caching through online learning. IEEE Trans. Multimedia 18, 2503–2516 (2016)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad (CUI), Abbottabad Campus, Pakistan

    Muhammad Yasir, Sardar Khaliq uz Zaman, Tahir Maqsood, Faisal Rehman & Saad Mustafa

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  1. Muhammad Yasir
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  2. Sardar Khaliq uz Zaman
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  3. Tahir Maqsood
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  4. Faisal Rehman
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  5. Saad Mustafa
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All authors contributed equally.

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Correspondence to Tahir Maqsood.

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Yasir, M., uz Zaman, S.K., Maqsood, T. et al. CoPUP: content popularity and user preferences aware content caching framework in mobile edge computing. Cluster Comput 26, 267–281 (2023). https://doi.org/10.1007/s10586-022-03624-0

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