Ultra-dense edge caching under spatio-temporal demand and network dynamics
By Hyesung Kim, Jihong Park, Medhi Bennis, Seong-Lyun Kim, Merouane Debbah. 2017
27 citations recorded by the source corpus.
This paper investigates a cellular edge caching design under an extremely large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), SBS-user distances shrink, and each user can request a cached content from multiple SBSs. Unfortunately, the complexity of existing caching controls' mechanisms increases with the number of SBSs, making them inapphcable for solving the fundamental caching problem: How to maximize local caching gain while minimizing the replicated content caching? Furthermore, spatial dynamics of interference is no longer negligible in UDCNs due to the surge in interference. In addition, the caching control should consider temporal dynamics of user demands. To overcome such difficulties, we propose a novel caching algorithm weaving together notions of mean-field game theory and stochastic geometry. These enable our caching algorithm to become independent of the number of SBSs and users, while incorporating spatial interference dynamics as well as temporal dynamics of content popularity and storage constraints. Numerical evaluation validates the fact that the proposed algorithm reduces not only the long run average cost by at least 24% but also the number of replicated content by 56% compared to a popularity-based algorithm.
Subfields: Computer Networks and Communications
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Medhi Bennis
- Jihong Park
- Merouane Debbah
- Sumudu Samarakoon
- Georgios B. Giannakis
- Alireza Sadeghi
- Fatemeh Sheikholeslami
- Walid Saad
Citations in the corpus
- Wireless Network Intelligence at the Edge (2018, methodology): “Moreover, the spatio-temporal content popularity dynamics were modeled in [167], and an optimal file caching strategy was found using MFG.”
- Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities (2017, methodology): “To accommodate dynamics, Ornstein-Uhlenbeck processes and Poisson shot noise models are utilized in [12] and [13], respectively, while context- and trend-aware caching approaches are investigated in [14] and [15].”