We take a systematic look at the problem of storing whole files in a cache with limited capacity in the context of optimistic learning, where the caching policy has access to a prediction oracle (provided by, e.g., a Neural Network). The successive file requests are assumed to be generated by an adversary, and no assumption is made on the accuracy of the oracle. In this setting, we provide a universal lower bound for prediction-assisted online caching and proceed to design a suite of policies with a range of performance-complexity trade-offs. All proposed policies offer sublinear regret bounds commensurate with the accuracy of the oracle. Our results substantially improve upon all recently-proposed online caching policies, which, being unable to exploit the oracle predictions, offer only $O(\sqrt{T})$ regret. In this pursuit, we design, to the best of our knowledge, the first comprehensive optimistic Follow-the-Perturbed leader policy, which generalizes beyond the caching problem. We also study the problem of caching files with different sizes and the bipartite network caching problem. Finally, we evaluate the efficacy of the proposed policies through extensive numerical experiments using real-world traces.
翻译:在乐观学习的背景下,我们系统地研究将整个文件储存在一个能力有限的缓存库中的问题,在这种缓存政策能够取得预测或触礁(例如由神经网络提供)的情况下,我们系统地研究将整个文件储存在一个能力有限的缓存库中的问题。 连续的文件请求假定是由对手提出,没有假设神器的准确性。 在这种背景下,我们为预测辅助的在线缓存提供了一个普遍较低的约束,并着手设计一套具有一系列性能兼容性交易的政策。所有拟议的政策都提供了与神器准确性相称的次线性遗憾界限。我们的结果大大改进了所有最近提出的在线缓存政策,这些政策无法利用神器预测,只能提供$O(sqrt{T})的遗憾。在这种追求中,我们根据我们所知,设计了第一个全面乐观的 " 跟踪者 " 领导者政策,该政策超越了缓存问题的范围。我们还研究了以不同大小的缓存档案和双端网络追踪问题为主的问题。最后,我们用真实的轨迹评估了拟议政策的有效性。