Cache

Computer network

Mathematics

Cache algorithms

Structural engineering

Biology

Reinforcement learning

Distributed computing

The Internet

World Wide Web

Engineering

Mathematical analysis

Paleontology

Artificial intelligence

CPU cache

Context (archaeology)

Computer science

False sharing

Scheme (mathematics)

Content delivery

Scalability

Node (physics)

Database

IEEE Transactions on Cognitive Communications and Networking

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.

Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks