This paper presents an adaptive per-file caching policy designed to dynamically adjust caching decisions based on the importance of the requested files. It relies on the Online Gradient Ascent (OGA) algorithm, which treats the caching problem as an online optimization problem. This methodology ensures minimal regret by continuously optimizing caching configurations in response to real-time request sequences. The caching configurations are optimized after every request using a constant learning rate. Because the trends of requested files can change, we will introduce two new algorithms that change the learning rate at every request to increase the adaptability. We will present two new algorithms, the Universally Adaptive Caching (UAC) algorithm and the Adaptive Per File Caching Algorithm (APFC), and will present scenarios to highlight their performances.

This paper investigates strategies to limit the cost of switching the cache in the context of an optimistic discrete caching problem. We have chosen as a starting point the current state-of-the-art in optimistic discrete caching, the Optimistic Follow-The-Perturbed-Leader (OFTPL) algorithm. We propose two strategies that attempt to limit this cost. The first approach sets a lower bound for the perturbation factor, thus including a mandatory amount of noise in the optimization of the cache state. The second approach investigates dynamically updating the perturbation factor to increase the switching cost, hence allowing the algorithm to adapt to the scenario at hand. Therefore, to the best of our knowledge, we design the first optimistic discrete caching algorithm that attempts to optimize the switching cost. Finally, we experiment and evaluate our solutions while highlighting their strengths and limitations.

In recent years, the novel framing of the caching problem as an Online Convex Optimisation (OCO) problem has led to the introduction of several online caching policies. These policies are proven optimal with regard to regret for any arbitrary request pattern, including that of adversarial origin. Nevertheless, their performance is primarily affected by the tuning of their so-called hyperparameters (e.g. learning rate), which is often done under the presumption of adversarial conditions. Consequently, as not all request patterns encountered in practice are adversarial, this suggests potential for further improvements. Unfortunately, the tuning of these hyperparameters to achieve optimal performance across non-adversarial request sequences remains dubious and poses a new challenge. This paper proposes an online meta-learner that combines several instances of the OGA caching policy, each distinct in their learning rate, framing the problem as an expert advice decision problem. The proposed meta-learner dynamically shifts between caching experts and achieves a regret upper bound similar to that of the best-performing caching expert. The penalty introduced for learning the best expert is limited to a logarithmic dependence on the number of experts, which improves upon previous works. Numerical evaluations composed of synthetic request traces demonstrate consistent improvements when the caching experts' relative performance varies over time.

This paper explores algorithms to optimize networked caching, where requests for files can be handled by a local cache instead of a remote server. Caches work collaboratively to prevent redundant caching, and each new batch of file requests is used to update the entire network. Data is cached in an integral manner, meaning that only discrete files or chunks can be stored, not fractions of them. Bipartite networks are studied, though the proposed model supports arbitrary network topologies. The approach is based upon an Online Mirror Descent (OMD) policy, which has been shown to have sublinear regret in single cache scenarios.