Predictive Edge Caching through Deep Mining of Sequential Patterns in User Content Retrievals

2022-10-06 · Chen Li, Xiaoyu Wang, Tongyu Zong, Houwei Cao, Yong liu

Edge caching plays an increasingly important role in boosting user content retrieval performance while reducing redundant network traffic. The effectiveness of caching ultimately hinges on the accuracy of predicting content popularity in the near future. However, at the network edge, content popularity can be extremely dynamic due to diverse user content retrieval behaviors and the low-degree of user multiplexing. It's challenging for the traditional reactive caching systems to keep up with the dynamic content popularity patterns. In this paper, we propose a novel Predictive Edge Caching (PEC) system that predicts the future content popularity using fine-grained learning models that mine sequential patterns in user content retrieval behaviors, and opportunistically prefetches contents predicted to be popular in the near future using idle network bandwidth. Through extensive experiments driven by real content retrieval traces, we demonstrate that PEC can adapt to highly dynamic content popularity, and significantly improve cache hit ratio and reduce user content retrieval latency over the state-of-art caching policies. More broadly, our study demonstrates that edge caching performance can be boosted by deep mining of user content retrieval behaviors.

📄 PDF Abstract BibTeX arXiv:2210.02657

Code (0)

등록된 구현이 없습니다.

Tasks

Retrieval

Fine-Grained AI Model Caching and Downloading With Coordinated Multipoint Broadcasting in Multi-Cell Edge Networks

2025-09-16 · Yang Fu, Peng Qin, Yueyue Zhang, Pao Cheng 외 arxiv

6G networks are envisioned to support on-demand AI model downloading to accommodate diverse inference requirements of end users. By proactively caching models at edge nodes, users can retrieve the requested models with l…

Data Augmentation

Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning

2021-01-14 · Tongyu Zong, Chen Li, Yuanyuan Lei, Guangyu Li 외

Edge caching will play a critical role in facilitating the emerging content-rich applications. However, it faces many new challenges, in particular, the highly dynamic content popularity and the heterogeneous caching con…

Deep Reinforcement Learning Ensemble Learning Time Series Time Series Analysis

SenCache: Accelerating Diffusion Model Inference via Sensitivity-Aware Caching

2026-02-27 · Yasaman Haghighi, Alexandre Alahi arxiv

Diffusion models achieve state-of-the-art video generation quality, but their inference remains expensive due to the large number of sequential denoising steps. This has motivated a growing line of research on accelerati…

Video Generation

Joint Optimization of Base Station Clustering and Service Caching in User-Centric MEC

2023-02-21 · Langtian Qin, Hancheng Lu, Yao Lu, Chenwu Zhang 외

Edge service caching can effectively reduce the delay or bandwidth overhead for acquiring and initializing applications. To address single-base station (BS) transmission limitation and serious edge effect in traditional …

Clustering

Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks

2019-05-13 · Chen Zhong, M. Cenk Gursoy, Senem Velipasalar

The growing demand on high-quality and low-latency multimedia services has led to much interest in edge caching techniques. Motivated by this, we in this paper consider edge caching at the base stations with unknown cont…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)