Commonly used caching policies, such as LRU (Least Recently Used) or LFU (Least Frequently Used), exhibit optimal performance only under specific traffic patterns. Even advanced machine learning-based methods, which detect patterns in historical request data, struggle when future requests deviate from past trends. Recently, a new class of policies has emerged that are robust to varying traffic patterns. These algorithms address an online optimization problem, enabling continuous adaptation to the context. They offer theoretical guarantees on the regret metric, which measures the performance gap between the online policy and the optimal static cache allocation in hindsight. However, the high computational complexity of these solutions hinders their practical adoption. In this study, we introduce a new variant of the gradient-based online caching policy that achieves groundbreaking logarithmic computational complexity relative to catalog size, while also providing regret guarantees. This advancement allows us to test the policy on large-scale, real-world traces featuring millions of requests and items - a significant achievement, as such scales have been beyond the reach of existing policies with regret guarantees. To the best of our knowledge, our experimental results demonstrate for the first time that the regret guarantees of gradient-based caching policies offer substantial benefits in practical scenarios.

翻译：常用的缓存策略（如最近最少使用（LRU）或最不经常使用（LFU））仅在特定流量模式下表现出最优性能。即使是基于机器学习的高级方法，虽然能检测历史请求数据中的模式，当未来请求偏离过去趋势时也会面临困难。最近，出现了一类对多变流量模式具有鲁棒性的新策略。这些算法解决了一个在线优化问题，能够持续适应上下文环境。它们在遗憾度量上提供了理论保证，该度量衡量了在线策略与事后最优静态缓存分配之间的性能差距。然而，这些解决方案的高计算复杂度阻碍了其实际应用。在本研究中，我们引入了一种基于梯度的在线缓存策略的新变体，它实现了相对于目录规模突破性的对数计算复杂度，同时仍提供遗憾保证。这一进展使我们能够在包含数百万请求和项目的大规模真实世界轨迹上测试该策略——这是一项重要成就，因为如此大的规模此前是现有具有遗憾保证的策略所无法企及的。据我们所知，我们的实验结果首次证明，基于梯度的缓存策略的遗憾保证在实际场景中能带来显著益处。