Year: 2025
Type: article
Abstract: Online learning algorithms have been successfully used to design caching policies with sublinear regret in the total number of requests, with no statistical assumption about the request sequence. Most existing algorithms involve computationally expensive operations and require knowledge of all past requests. However, this may not be feasible in practical scenarios such asfemtocaching, where a base station (BS) jointly decides the content of many edge caches and visibility of all requests at the BS requires constant communication between these caches and the BS. To capture this constraint, we study a single cache problem under a more restrictive setting, that we refer to as the Bernoulli Partial Observability (BPO) model, in which the caching policy only observes a request with probability$p$, reflecting the fraction of requests forwarded from the edge caches to the BS in thefemtocachingexample. We propose a policy, based on the classic online learning algorithm Follow-the-Perturbed-Leader (FPL), that achieves an asymptotically optimal regret bound of$\mathcal {O}(\sqrt {CT/p})$under BPO in$\mathcal {O}(1)$amortized time complexity as$T$goes to infinity, where$C$is the cache size and$T$is the number of requests. Moreover, we show that our policy extends to bipartite caching albeit with a sublinear$\alpha $-regret for$\alpha =1-1/e$and a higher computational cost. The experimental evaluation compares the proposed solution with classic caching policies and validates the proposed approach using both synthetic and real-world request traces.
Source: IEEE Transactions on Networking
Institutions: Université d'Avignon et des Pays de Vaucluse, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université de Lille, Centre de Recherche en Informatique, Signal et Automatique de Lille, NEO: Ingénierie et Opérations des Réseaux
Language: English
Topic: Caching and Content Delivery
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4416961310