Adaptive Cache Deployment Based on Congestion Awareness and Content Value in LEO Satellite Networks
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摘要: 低地球轨道(LEO)卫星网络凭借其全球无缝覆盖与低传输延迟的优势，被视为空天地一体化通信系统的关键组成部分。然而，用户请求若未命中本地卫星缓存，则需通过星间链路或星地链路回源，从而引入较高延迟。同时，受限于星载硬件资源，卫星节点的存储容量极为有限，难以支持大规模内容缓存，这对动态自适应的缓存部署机制设计提出了严峻挑战。该文聚焦LEO卫星网络中的缓存部署问题，基于拥塞感知和内容价值，提出一种自适应缓存部署方案，根据网络的实时状态实施缓存决策，从而提升缓存命中率、降低星地回传负载、优化用户服务质量。首先，卫星节点实时监测链路拥塞状态，并将链路拥塞的概率通过数据包反馈给下游节点；其次，结合兴趣包的内容流行度、数据包的新鲜度构建二维价值评估模型；最后，依据拥塞等级动态调整缓存阈值，再结合跳数控制因子进行缓存决策。仿真结果表明，所提策略在缓存命中率、平均路由跳数及平均请求时延3项核心指标上均优于基于流行度感知的邻近度缓存(PaCC)与处处缓存(LCE)策略：在缓存容量动态变化场景下，缓存命中率较PaCC和LCE策略分别提升9.5%和43.7%；在Zipf分布参数变化场景下，缓存命中率较上述2种策略分别提升8.7%和29.1%；在网络传输性能方面，所提策略的平均路由跳数较PaCC策略总体下降2.24%，平均请求时延则较PaCC和LCE策略总体下降2.8%和9.5%。Abstract:
Objective Low Earth Orbit (LEO) satellite networks are central to future space–air–ground integrated systems, offering global coverage and low-latency communication. However, their high-speed mobility leads to rapidly changing topologies, and strict onboard cache constraints hinder efficient content delivery. Existing caching strategies often overlook real-time network congestion and content attributes (e.g., freshness), which leads to inefficient resource use and degraded Quality of Service (QoS). To address these limitations, we propose an adaptive cache placement strategy based on congestion awareness. The strategy dynamically couples real-time network conditions, including link congestion and latency, with a content value assessment model that incorporates both popularity and freshness.This integrated approach enhances cache hit rates, reduces backhaul load, and improves user QoS in highly dynamic LEO satellite environments, enabling efficient content delivery even under fluctuating traffic demands and resource constraints. Methods The proposed strategy combines a dual-threshold congestion detection mechanism with a multi-dimensional content valuation model. It proceeds in three steps. First, satellite nodes monitor link congestion in real time using dual latency thresholds and relay congestion status to downstream nodes through data packets. Second, a two-dimensional content value model is constructed that integrates popularity and freshness. Popularity is updated dynamically using an Exponential Weighted Moving Average (EWMA), which balances historical and recent request patterns to capture temporal variations in demand. Freshness is evaluated according to the remaining data lifetime, ensuring that expired or near-expired content is deprioritized to maintain cache efficiency and relevance. Third, caching thresholds are adaptively adjusted according to congestion level, and a hop count control factor is introduced to guide caching decisions. This coordinated mechanism enables the system to prioritize high-value content while mitigating congestion, thereby improving overall responsiveness and user QoS. Results and Discussions Simulations conducted on ndnSIM demonstrate the superiority of the proposed strategy over PaCC (Popularity-Aware Closeness-based Caching), LCE (Leave Copy Everywhere), LCD (Leave Copy Down), and Prob (probability-based caching with probability = 0.5). The key findings are as follows. (1) Cache hit rate. The proposed strategy consistently outperforms conventional methods. As shown in Fig. 8 , the cache hit rate rises markedly with increasing cache capacity and Zipf parameter, exceeding those of LCE, LCD, and Prob. Specifically, the proposed strategy achieves improvements of 43.7% over LCE, 25.3% over LCD, 17.6% over Prob, and 9.5% over PaCC. Under high content concentration (i.e., larger Zipf parameters), the improvement reaches 29.1% compared with LCE, highlighting the strong capability of the strategy in promoting high-value content distribution. (2) Average routing hop ratio. The proposed strategy also reduces routing hops compared with the baselines. As shown inFig. 9 , the average hop ratio decreases as cache capacity and Zipf parameter increase. Relative to PaCC, the proposed strategy lowers the average hop ratio by 2.24%, indicating that content is cached closer to users, thereby shortening request paths and improving routing efficiency. (3) Average request latency. The proposed strategy achieves consistently lower latency than all baseline methods. As summarized inTable 2 andFig. 10 , the reduction is more pronounced under larger cache capacities and higher Zipf parameters. For instance, with a cache capacity of 100 MB, latency decreases by approximately 2.9%, 5.8%, 9.0%, and 10.3% compared with PaCC, Prob, LCD, and LCE, respectively. When the Zipf parameter is 1.0, latency reductions reach 2.7%, 5.7%, 7.2%, and 8.8% relative to PaCC, Prob, LCD, and LCE, respectively. Concretely, under a cache capacity of 100 MB and Zipf parameter of 1.0, the average request latency of the proposed strategy is 212.37 ms, compared with 236.67 ms (LCE), 233.45 ms (LCD), 225.42 ms (Prob), and 218.62 ms (PaCC).Conclusions This paper presents a congestion-aware adaptive caching placement strategy for LEO satellite networks. By combining real-time congestion monitoring with multi-dimensional content valuation that considers both dynamic popularity and freshness, the strategy achieves balanced improvements in caching efficiency and network stability. Simulation results show that the proposed method markedly enhances cache hit rates, reduces average routing hops, and lowers request latency compared with existing schemes such as PaCC, Prob, LCD, and LCE. These benefits hold across different cache sizes and request distributions, particularly under resource-constrained or highly dynamic conditions, confirming the strategy’s adaptability to LEO environments. The main innovations include a closed-loop feedback mechanism for congestion status, dynamic adjustment of caching thresholds, and hop-aware content placement, which together improve resource utilization and user QoS. This work provides a lightweight and robust foundation for high-performance content delivery in satellite–terrestrial integrated networks. Future extensions will incorporate service-type differentiation (e.g., delay-sensitive vs. bandwidth-intensive services), and orbital prediction to proactively optimize cache migration and updates, further enhancing efficiency and adaptability in 6G-enabled LEO networks. -
表 1 内容流行度记录表
内容名称 请求数量 上一时间段综合内容流行度 当前综合内容流行度 A. mp4 56 0.51 0.57 B. jpg 42 0.46 0.53 C. com 30 0.37 0.42 ··· ··· ··· ··· 
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表 2 平均请求时延与缓存容量关系表
缓存容量(MB) 平均请求时延(ms) 本文方法 PaCC Prob LCD LCE 10 278.33 281.58 287.25 291.56 295.27 50 243.76 250.46 258.36 262.74 267.38 80 223.51 229.27 234.74 239.62 243.54 100 212.37 218.62 225.42 233.45 236.67
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