Abstract
Small base stations (SBs) of fifth-generation (5G) cellular networks are envisioned to have storage devices to locally serve requests for reusable and popular contents by caching them at the edge of the network, close to the end users. The ultimate goal is to smartly utilize a limited storage capacity to serve locally contents that are frequently requested instead of fetching them from the cloud, contributing to a better overall network performance and service experience. To enable the SBs with efficient fetch-cache decision-making schemes operating in dynamic settings, this paper introduces simple but flexible generic time-varying fetching and caching costs, which are then used to formulate a constrained minimization of the aggregate cost across files and time. Since caching decisions per time slot influence the content availability in future slots, the novel formulation for optimal fetch-cache decisions falls into the class of dynamic programming. Under this generic formulation, first by considering stationary distributions for the costs as well as file popularities, an efficient reinforcement learning-based solver known as value iteration algorithm can be used to solve the emerging optimization problem. Later, it is shown that practical limitations on cache capacity can be handled using a particular instance of this generic dynamic pricing formulation. Under this setting, to provide a light-weight online solver for the corresponding optimization, the well-known reinforcement learning algorithm, Q -learning, is employed to find optimal fetch-cache decisions. Numerical tests corroborating the merits of the proposed approach wrap up the paper.
| Original language | English (US) |
|---|---|
| Article number | 8790766 |
| Pages (from-to) | 2267-2281 |
| Number of pages | 15 |
| Journal | IEEE Journal on Selected Areas in Communications |
| Volume | 37 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2019 |
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Keywords
- Dynamic caching
- Q-learning
- dynamic programming
- fetching
- value iteration
Cite this
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing. / Sadeghi, Alireza; Sheikholeslami, Fatemeh; Marques, Antonio G.; Giannakis, Georgios B.
In: IEEE Journal on Selected Areas in Communications, Vol. 37, No. 10, 8790766, 10.2019, p. 2267-2281.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing
AU - Sadeghi, Alireza
AU - Sheikholeslami, Fatemeh
AU - Marques, Antonio G.
AU - Giannakis, Georgios B
PY - 2019/10
Y1 - 2019/10
N2 - Small base stations (SBs) of fifth-generation (5G) cellular networks are envisioned to have storage devices to locally serve requests for reusable and popular contents by caching them at the edge of the network, close to the end users. The ultimate goal is to smartly utilize a limited storage capacity to serve locally contents that are frequently requested instead of fetching them from the cloud, contributing to a better overall network performance and service experience. To enable the SBs with efficient fetch-cache decision-making schemes operating in dynamic settings, this paper introduces simple but flexible generic time-varying fetching and caching costs, which are then used to formulate a constrained minimization of the aggregate cost across files and time. Since caching decisions per time slot influence the content availability in future slots, the novel formulation for optimal fetch-cache decisions falls into the class of dynamic programming. Under this generic formulation, first by considering stationary distributions for the costs as well as file popularities, an efficient reinforcement learning-based solver known as value iteration algorithm can be used to solve the emerging optimization problem. Later, it is shown that practical limitations on cache capacity can be handled using a particular instance of this generic dynamic pricing formulation. Under this setting, to provide a light-weight online solver for the corresponding optimization, the well-known reinforcement learning algorithm, Q -learning, is employed to find optimal fetch-cache decisions. Numerical tests corroborating the merits of the proposed approach wrap up the paper.
AB - Small base stations (SBs) of fifth-generation (5G) cellular networks are envisioned to have storage devices to locally serve requests for reusable and popular contents by caching them at the edge of the network, close to the end users. The ultimate goal is to smartly utilize a limited storage capacity to serve locally contents that are frequently requested instead of fetching them from the cloud, contributing to a better overall network performance and service experience. To enable the SBs with efficient fetch-cache decision-making schemes operating in dynamic settings, this paper introduces simple but flexible generic time-varying fetching and caching costs, which are then used to formulate a constrained minimization of the aggregate cost across files and time. Since caching decisions per time slot influence the content availability in future slots, the novel formulation for optimal fetch-cache decisions falls into the class of dynamic programming. Under this generic formulation, first by considering stationary distributions for the costs as well as file popularities, an efficient reinforcement learning-based solver known as value iteration algorithm can be used to solve the emerging optimization problem. Later, it is shown that practical limitations on cache capacity can be handled using a particular instance of this generic dynamic pricing formulation. Under this setting, to provide a light-weight online solver for the corresponding optimization, the well-known reinforcement learning algorithm, Q -learning, is employed to find optimal fetch-cache decisions. Numerical tests corroborating the merits of the proposed approach wrap up the paper.
KW - Dynamic caching
KW - Q-learning
KW - dynamic programming
KW - fetching
KW - value iteration
UR - http://www.scopus.com/inward/record.url?scp=85070720524&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070720524&partnerID=8YFLogxK
U2 - 10.1109/JSAC.2019.2933780
DO - 10.1109/JSAC.2019.2933780
M3 - Article
AN - SCOPUS:85070720524
VL - 37
SP - 2267
EP - 2281
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
SN - 0733-8716
IS - 10
M1 - 8790766
ER -