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A novel user preference-aware content caching algorithm in mobile edge networks

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Abstract

One of the most important strategies used to mitigate the adverse impacts of traffic growth on mobile networks is caching. By caching at the edge, the backhaul traffic load is reduced, and the quality of service for the user is increased. Develo** an effective caching algorithm requires accurate prediction of the future popularity of the content, which is a challenging issue. In recent years, deep learning models have achieved high predictive accuracy due to advancements in data availability and increased computing power. In this paper, we present a caching algorithm called the user preference-aware content caching algorithm (UPACA). This algorithm is specifically designed for an edge content delivery platform where users can access content services provided by a remote content provider. UPACA operates in two steps. In the first step, the proposed collaborative filtering-based popularity prediction algorithm (CFPA) is used to predict future content popularities. CFPA utilizes a gated residual variational autoencoder collaborative filtering model to predict users’ future preferences and calculate the future popularity of content. This algorithm considers the popularity of the content as well as the number and timing of content requests. Experimental results demonstrate that UPACA outperforms previous methods in terms of cache hit rates and user utilities.

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Notes

  1. Contents with varying sizes can be effectively managed by partitioning them into data segments of uniform size. Each of these data segments can then be treated as an individual content. This is a common practice in real-world systems.

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Authors and Affiliations

  1. Faculty of Computer Engineering, University of Isfahan, Isfahan, 81746-73441, Iran

    Mostafa Taghizade Firouzjaee, Kamal Jamshidi & Neda Moghim

  2. Department of Computational Modeling and Simulation Engineering, Virginia Modeling, Analysis, and Simulation Center, Old Dominion University, Suffolk, VA, 23435, USA

    Neda Moghim

Authors
  1. Mostafa Taghizade Firouzjaee
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  2. Kamal Jamshidi
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  3. Neda Moghim
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Contributions

MTF, Programmer, contributed to the software, validation, conceptualization, visualization, investigation, writing—reviewing and editing, writing—original draft preparation. KJ, Corresponding author, was involved in the supervision, project administration, conceptualization, methodology, visualization, investigation, writing—reviewing and editing. NM assisted in the supervision, conceptualization, methodology, visualization, investigation, writing—reviewing and editing.

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Correspondence to Kamal Jamshidi.

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Taghizade Firouzjaee, M., Jamshidi, K. & Moghim, N. A novel user preference-aware content caching algorithm in mobile edge networks. J Supercomput 80, 12273–12296 (2024). https://doi.org/10.1007/s11227-023-05860-6

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