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.

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 explores the effect of forecasting methods on the Optimistic Follow The Regularized Leader (OFTRL) caching policy. It has been theoretically proven that the performance of OFTRL improves with accurate forecasters. However, the forecasters were portrayed as black boxes. In this paper, we do not treat forecasters as a black box but rather use recommender systems and temporal convolutional networks (TCN) implementations as forecasters for OFTRL in the caching context. The said forecasters predict the next file requests directly from the request trace rather than monitoring popularity. Using request traces extracted from the well-known MovieLens dataset, it was found that incorporating a forecaster into an optimistic learning algorithm is not straightforward. In fact, it was found that simpler approaches such as one that predicts the most requested file, can outperform more complicated deep learning models in terms of regret even if they possess a lower accuracy.