An Online Gradient-Based Caching Policy with Logarithmic Complexity and Regret Guarantees

arXiv:2405.01263v1 Announce Type: new
Abstract: The commonly used caching policies, such as LRU or LFU, exhibit optimal performance only for 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 makes no assumptions about the request arrival process. These algorithms solve an online optimization problem, enabling continuous adaptation to the context. They offer theoretical guarantees on the regret …

abstract advanced arxiv caching class complexity cs.lg cs.ni cs.os data future gradient machine machine learning patterns performance policies policy request struggle traffic trends type