NM

N. Mhaisen

4 records found

Machine Learning Algorithms for Caching Systems

Online Learning for Caching with Heterogeneous miss-costs

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. Th ...

Meta-learning the Best Caching Expert

Tuning caching policies with expert advice

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 ad ...

Optimistic Discrete Caching with Switching Costs

Machine Learning Algorithms for Caching Systems

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) ...
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. D ...