Caching is envisioned to play a critical role in next-generation content delivery infrastructure, cellular networks, and Internet architectures. By smartly storing the most popular contents at the storage-enabled network entities during off-peak demand instances, caching can benefit both network infrastructure as well as end users, during on-peak periods. In this context, distributing the limited storage capacity across network entities calls for decentralized caching schemes. Many practical caching systems involve a parent caching node connected to multiple leaf nodes to serve user file requests. To model the two-way interactive influence between caching decisions at the parent and leaf nodes, a reinforcement learning (RL) framework is put forth. To handle the large continuous state space, a scalable deep RL approach is pursued. The novel approach relies on a hyper-deep Q-network to learn the Q-function, and thus the optimal caching policy, in an online fashion. Reinforcing the parent node with ability to learn-and-adapt to unknown policies of leaf nodes as well as spatio-temporal dynamic evolution of file requests, results in remarkable caching performance, as corroborated through numerical tests.
|Original language||English (US)|
|Number of pages||10|
|Journal||IEEE Transactions on Cognitive Communications and Networking|
|State||Published - Dec 2019|
Bibliographical noteFunding Information:
Manuscript received February 26, 2019; revised June 30, 2019; accepted August 10, 2019. Date of publication August 20, 2019; date of current version December 12, 2019. This work was supported in part by NSF grants 1711471, 1514056, and 1901134. The associate editor coordinating the review of this article and approving it for publication was H. T. Dinh. (Corresponding author: Gang Wang.) The authors are with the Digital Technology Center, University of Minnesota, Minneapolis, MN 55455 USA, and also with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org). Digital Object Identifier 10.1109/TCCN.2019.2936193
© 2015 IEEE.
- deep Q-network
- deep RL
- function approximation
- next-generation networks