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A Deep Reinforcement Learning-Based Framework for Content Caching
Chen Zhong, M. Cenk Gursoy, Senem Velipasalar
TL;DR
Growing multimedia traffic creates a need for cache-replacement policies that manage which contents are stored at the edge. The paper develops a Wolpertinger-based DRL framework for base-station caching and reports improved short- and long-term cache-hit performance versus LRU, LFU, and FIFO, with competitive cache rates and lower runtime than DQN.
Problem
Content caching requires choosing which contents to store, while prior approaches rely heavily on content popularity distributions that can change at large scale.
Method
The paper develops a DRL content-caching framework using Wolpertinger architecture to make cache-replacement decisions and limit the action space.
Results
The framework improves short-term and stable long-term cache hit rates versus LRU, LFU, and FIFO, while achieving competitive cache rates with significant runtime advantages over deep Q-learning.
Takeaways & Limitations
The proposed framework provides an efficient approach for large-scale content caching while requiring no knowledge of the content popularity distribution.
Takeaways & Limitations
The study considers only a single base station or access point, assumes equal content sizes, and does not explicitly address individual user preferences.
Abstract
from arXiv · showhide
Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, this work presents a DRL-based framework with Wolpertinger architecture for content caching at the base station. The proposed framework is aimed at maximizing the long-term cache hit rate, and it requires no knowledge of the content popularity distribution. To evaluate the proposed framework, we compare the performance with other caching algorithms, including Least Recently Used (LRU), Least Frequently Used (LFU), and First-In First-Out (FIFO) caching strategies. Meanwhile, since the Wolpertinger architecture can effectively limit the action space size, we also compare the performance with Deep Q-Network to identify the impact of dropping a portion of the actions. Our results show that the proposed framework can achieve improved short-term cache hit rate and improved and stable long-term cache hit rate in comparison with LRU, LFU, and FIFO schemes. Additionally, the performance is shown to be competitive in comparison to Deep Q-learning, while the proposed framework can provide significant savings in runtime.
I. INTRODUCTION
The paper frames edge content caching as a response to growing multimedia traffic and formulates DRL-based cache replacement at a base station. Its Wolpertinger framework improves cache-hit performance against standard policies and remains competitive with DQN while reducing runtime.
- Growing real-time multimedia content has congested content-server traffic and degraded user experience.
- Content caching pre-caches data at end users or base stations to offload traffic and save transmission time and resources.
- The proposed DRL agent learns cache replacement decisions at an edge node to maximize cache hit rate.
- Compared with LRU, LFU, and FIFO, the DRL agent achieves improved short-term and improved, stable long-term cache hit rates.
- Compared with deep Q-network, the DRL agent achieves competitive cache hit rates with significant runtime advantages.
II. RELATED WORK
Prior content-caching studies commonly rely on content popularity, whose changing distribution becomes challenging at large scale. The paper therefore motivates deep reinforcement learning, including methods designed for large discrete action spaces.
- Content popularity distribution is described as central to solving the content-caching problem in previous studies.
- Large-scale content collections and changing popularities motivate deep reinforcement learning as an attractive strategy for caching.
- Prior work includes multi-step return actor-critic, deep deterministic policy gradient, and Wolpertinger policies for reinforcement learning.
III. SYSTEM MODEL
The system models request-driven caching at a single base station with fixed capacity and equal-sized contents. The DRL agent chooses whether to cache each requested item and which cached item to replace, using cache hit rate as reward-related feedback.
- Requests trigger a DRL agent to cache frequently requested contents locally, serving hits directly and fetching misses from the original server.
- The model assumes one base station with cache size C, fixed per-slot request count N, unique content IDs, and equal-sized contents.
- For each request, the agent decides whether to store the requested content and, if so, which local content to replace.
- The action space contains cache-state exchanges between content pairs and an unchanged-cache action, but its size is limited to reduce computational complexity.
- Because all requests have equal-sized contents and no user priorities, data-traffic reduction is evaluated through cache hit rate.
- The framework seeks a policy σ* that maximizes the long-term cache hit rate under fixed cache capacity.
IV. DRL-BASED CONTENT CACHING FRAMEWORK
The proposed framework uses Wolpertinger architecture to narrow the action space in DRL-based content caching. This is intended to address high-dimensional state and action spaces while avoiding missed optimal policies.
- The framework maximizes cache hit rate to reduce data traffic in content caching.
- Wolpertinger architecture narrows the action space for high-dimensional caching problems while aiming to avoid missing the optimal policy.
A. Algorithm Overview
The framework combines an actor network, K-nearest-neighbors action expansion, and a critic network trained with DDPG to select cache-replacement actions. This Wolpertinger design reduces large-action-space computation while refining the actor’s choices using Q-values.
- The Wolpertinger framework consists of an actor network, K-nearest neighbors, and a critic network trained with DDPG.
- The actor: The actor maps each state to a proto-action and scales it so the result is a valid action.
- K-nearest neighbors: K-nearest-neighbors mapping expands the proto-action into a set of valid actions, reducing computation from the large action space.
- The critic: The critic evaluates Q-values for candidate actions using current and next states, and the maximum-Q action is selected.
- Update: The actor policy is updated using deep deterministic policy gradient.
B. Workflow
The workflow uses state features from recent content requests, constrained cache actions, and rewards combining short- and long-term cache hit rates. Networks are pretrained offline and then used for dynamic online control.
- The framework has offline and online phases for training and dynamically controlling the caching system.
- Offline phase: During offline training, actor and critic networks are pretrained with historic transition profiles and their parameters initialize online operation.
- Online phase: In the online phase, the agent observes the cache state, selects an action with an ε-greedy policy, executes it, and stores the resulting transition.
- State Space: The state contains cached-content and requested-content features summarizing requests over short-, medium-, and long-term periods.
- Action Space: The action space allows replacing one cached content with the requested content or keeping the cache unchanged, yielding C + 1 possible actions.
- Reward: The total reward combines short- and long-term rewards using weight w, with short-term reward defined from requests in the next epoch and long-term reward from the next 100 requests.
A. Simulation Setup
The simulations generate request data from Zipf distributions and evaluate fixed and changing popularity settings. Content-request counts from recent windows provide the network’s input features.
- The simulations use 5,000 files and 10,000 testing requests generated according to a Zipf distribution.
- The fixed-popularity dataset uses an unchanged Zipf popularity distribution with parameter 1.3.
- Network input features count requests for each file within the most recent 10, 100, and 1,000 requests.
B. Performance Comparison
The proposed DRL framework is evaluated against conventional caching policies and Deep Q-learning using short-term hit rate, long-term stability, and runtime. It achieves higher or competitive cache hit rates while reducing the action-evaluation burden.
- Evaluation setup: The evaluation compares cache hit rate with LRU, LFU, and FIFO across short-term capacity changes and long-term popularity shifts.The study also compares the framework with Deep Q-learning on cache hit rate and runtime.
- Short-term performance: The proposed framework provides a higher cache hit rate across the tested cache capacities, although all policies converge near 0.8 at C = 500.At high capacity, the cache can store all popular contents, so popularity distribution limits further gains.
- Long-term performance: As content popularity changes over time, the proposed framework initially requires adaptation, then reaches a peak and maintains a relatively stable cache hit rate.LFU drops rapidly because accumulated request frequencies become misleading after popularity changes, while LRU and FIFO remain stable but less competitive.
- Deep Q-learning comparison: The Wolpertinger-based framework achieves competitive cache hit rates compared with DQN while evaluating only a restricted set of candidate actions.K1 and K2 use k1 = ⌈0.15C⌉ and k2 = ⌈0.05C⌉ retrieved actions, respectively.
- Efficiency: At C = 300, DQN requires much more time per decision epoch than the proposed framework, whose lower runtime improves its suitability for large-scale problems.The comparison records average runtime for 1000 decision epochs.
VI. CONCLUSION
The paper develops and evaluates a Wolpertinger-based DRL caching policy, finding improved short- and long-term performance against conventional policies and competitive cache hit rates with lower runtime than DQN. The authors identify multi-base-station collaboration, unequal content sizes, and user preferences as future extensions.
- VI. CONCLUSION: The proposed content caching policy uses the Wolpertinger architecture and is trained with deep deterministic policy gradient.Its performance is evaluated against LRU, LFU, FIFO, and a deep Q-learning-based policy.
- VI. CONCLUSION: The framework improves both short-term and long-term cache hit-rate performance relative to LRU, LFU, and FIFO.The conclusion also reports competitive cache hit rates with effectively reduced runtime compared with deep Q-learning.
- VI. CONCLUSION: The current study considers only a single base station or access point, assumes equal content sizes, and does not explicitly address individual user preferences.The authors propose multi-base-station collaboration and incorporating these factors as future research directions.