arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2402.02795cs.NIcs.DCcs.LG
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一种基于学习的边缘内容分发缓存机制

A Learning-Based Caching Mechanism for Edge Content Delivery

York University(约克大学)

Hoda Torabi, Hamzeh Khazaei, Marin Litoiu

AI总结:

本文提出基于风险率排序的轻量级学习缓存框架HR-Cache,通过预测请求的缓存友好性指导边缘缓存逐出决策,实验表明其相比LRU可节省2.2%至14.6%的广域网流量,优于现有启发式与学习型缓存方法。

摘要:

随着5G网络的到来和物联网(IoT)的兴起,内容分发网络(CDNs)正日益向网络边缘延伸。这一转变带来了独特的挑战,尤其是由于边缘缓存存储有限以及请求模式多样。这些边缘环境可能承载着以不同对象大小分布和对象访问模式为特征的流量类别。这种复杂性使得传统缓存策略(通常依赖于请求频率或时间间隔等指标)难以有效发挥作用。尽管存在这些复杂性,边缘缓存的优化仍然至关重要。提高边缘的字节命中率不仅能减轻网络骨干的负载,还能最小化运营成本并加快向终端用户交付内容。在本文中,我们提出了HR-Cache,这是一个基于学习的综合缓存框架,其基础是风险率(Hazard Rate, HR)排序原则,该规则最初是为了计算缓存性能的上界而制定的。HR-Cache利用这一规则来指导未来的对象逐出决策。它采用一个轻量级机器学习模型,从基于HR排序的缓存决策中学习,进而预测传入请求的“缓存友好性”。被判定为“缓存不友好”的对象会被放入缓存并作为优先逐出的候选对象。通过大量实验,我们证明HR-Cache不仅与现有最先进的方法相比持续提高了字节命中率,而且以最小的预测开销实现了这一点。我们使用三个真实世界轨迹和一个合成轨迹的实验结果表明,HR-Cache持续实现了比LRU高出2.2%至14.6%的广域网(WAN)流量节省。它不仅优于启发式缓存策略,还优于最先进的基于学习的算法。

英文摘要:

With the advent of 5G networks and the rise of the Internet of Things (IoT), Content Delivery Networks (CDNs) are increasingly extending into the network edge. This shift introduces unique challenges, particularly due to the limited cache storage and the diverse request patterns at the edge. These edge environments can host traffic classes characterized by varied object-size distributions and object-access patterns. Such complexity makes it difficult for traditional caching strategies, which often rely on metrics like request frequency or time intervals, to be effective. Despite these complexities, the optimization of edge caching is crucial. Improved byte hit rates at the edge not only alleviate the load on the network backbone but also minimize operational costs and expedite content delivery to end-users. In this paper, we introduce HR-Cache, a comprehensive learning-based caching framework grounded in the principles of Hazard Rate (HR) ordering, a rule originally formulated to compute an upper bound on cache performance. HR-Cache leverages this rule to guide future object eviction decisions. It employs a lightweight machine learning model to learn from caching decisions made based on HR ordering, subsequently predicting the "cache-friendliness" of incoming requests. Objects deemed "cache-averse" are placed into cache as priority candidates for eviction. Through extensive experimentation, we demonstrate that HR-Cache not only consistently enhances byte hit rates compared to existing state-of-the-art methods but also achieves this with minimal prediction overhead. Our experimental results, using three real-world traces and one synthetic trace, indicate that HR-Cache consistently achieves 2.2-14.6% greater WAN traffic savings than LRU. It outperforms not only heuristic caching strategies but also the state-of-the-art learning-based algorithm.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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