Online learning algorithms have been successfully used to design caching policies with regret guarantees. Existing algorithms assume that the cache knows the exact request sequence, but this may not be feasible in high load and/or memory-constrained scenarios, where the cache may have access only to sampled requests or to approximate requests' counters. In this paper, we propose the Noisy-Follow-the-Perturbed-Leader (NFPL) algorithm, a variant of the classic Follow-the-Perturbed-Leader (FPL) when request estimates are noisy, and we show that the proposed solution has sublinear regret under specific conditions on the requests estimator. The experimental evaluation compares the proposed solution against classic caching policies and validates the proposed approach under both synthetic and real request traces.

翻译：在线学习算法已成功应用于设计具有遗憾保证的缓存策略。现有算法假设缓存知晓精确的请求序列，但在高负载和/或内存受限场景中，缓存可能仅能访问采样请求或近似请求计数器，这导致该假设难以实现。本文提出带噪跟随扰动领导者算法，它是经典FPL算法在请求估计存在噪声时的变体，并证明了在满足请求估计器特定条件下，所提方案具有次线性遗憾。实验评估将所提方案与经典缓存策略进行对比，并通过合成请求轨迹与真实请求轨迹验证了方法的有效性。