UniCache: A Unified Batch-Level Learning-Based Content Caching
Yanting Chen, Xu Zhang, Yuchen Yang, Chengying Huan, Shaonan Ma, Jiawei Ye, Peng Wang, Jie Wu · 2025
Content Delivery Networks (CDNs) rely heavily on caching algorithms to minimize content delivery latency and optimize network performance. While machine learning approaches have emerged as promising solutions for handling complex request patterns in caching systems, current learningbased caching methods face critical limitations in processing granularity and operational efficiency. Existing approaches either process requests in coarse-grained time windows or struggle with throughput bottlenecks during object-level operations. To address these challenges, we present UniCache, a novel batchlevel content caching algorithm that balances processing granularity and system efficiency. UniCache introduces a Batch Queue architecture coupled with specialized Batch-level Inference components, enabling high-throughput processing while providing fine-grained request information. This design prevents both suboptimal caching decisions and request accumulation delays during prediction phases. Furthermore, UniCache overcomes the common limitation of treating admission and eviction policies as separate entities, by implementing a unified model that jointly optimizes both processes based on object popularity patterns. The integration of tiered cache storage enhances the system's resilience to prediction inaccuracies while facilitating effective identification and retention of popular objects. Experimental evaluation on Wiki CDN and Tencent Photo datasets demonstrates that UniCache achieves$\mathbf{1 2 \%} \sim \mathbf{5 3 \%}$improvement in Object Hit Ratio (OHR) compared to state-of-the-art methods while maintaining real-time processing capabilities. Comprehensive ablation studies validate the effectiveness of each architectural component in the overall system design.