Reinforcement Learning for Caching with Space-Time Popularity Dynamics

• URL: http://arxiv.org/abs/2005.09155v1
• Date: Tue, 19 May 2020 01:23:51 GMT
• Title: Reinforcement Learning for Caching with Space-Time Popularity Dynamics
• Authors: Alireza Sadeghi and Georgios B. Giannakis and Gang Wang and Fatemeh Sheikholeslami
• Abstract summary: caching is envisioned to play a critical role in next-generation networks. To intelligently prefetch and store contents, a cache node should be able to learn what and when to cache. This chapter presents a versatile reinforcement learning based approach for near-optimal caching policy design.
• Score: 61.55827760294755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the tremendous growth of data traffic over wired and wireless networks along with the increasing number of rich-media applications, caching is envisioned to play a critical role in next-generation networks. To intelligently prefetch and store contents, a cache node should be able to learn what and when to cache. Considering the geographical and temporal content popularity dynamics, the limited available storage at cache nodes, as well as the interactive in uence of caching decisions in networked caching settings, developing effective caching policies is practically challenging. In response to these challenges, this chapter presents a versatile reinforcement learning based approach for near-optimal caching policy design, in both single-node and network caching settings under dynamic space-time popularities. The herein presented policies are complemented using a set of numerical tests, which showcase the merits of the presented approach relative to several standard caching policies.

Related papers

• Efficient Inference of Vision Instruction-Following Models with Elastic Cache [76.44955111634545]
  We introduce Elastic Cache, a novel strategy for efficient deployment of instruction-following large vision-language models. We propose an importance-driven cache merging strategy to prune redundancy caches. For instruction encoding, we utilize the frequency to evaluate the importance of caches. Results on a range of LVLMs demonstrate that Elastic Cache not only boosts efficiency but also notably outperforms existing pruning methods in language generation.
  arXiv  Detail & Related papers  (2024-07-25T15:29:05Z)
• A Learning-Based Caching Mechanism for Edge Content Delivery [2.412158290827225]
  5G networks and the rise of the Internet of Things (IoT) are increasingly extending into the network edge. This shift introduces unique challenges, particularly due to the limited cache storage and the diverse request patterns at the edge. We introduce HR-Cache, a learning-based caching framework grounded in the principles of Hazard Rate (HR) ordering.
  arXiv  Detail & Related papers  (2024-02-05T08:06:03Z)
• Predictive Edge Caching through Deep Mining of Sequential Patterns in User Content Retrievals [34.716416311132946]
  We propose a novel Predictive Edge Caching (PEC) system that predicts the future content popularity using fine-grained learning models. PEC can adapt to highly dynamic content popularity, and significantly improve cache hit ratio and reduce user content retrieval latency.
  arXiv  Detail & Related papers  (2022-10-06T03:24:19Z)
• Accelerating Deep Learning Classification with Error-controlled Approximate-key Caching [72.50506500576746]
  We propose a novel caching paradigm, that we named approximate-key caching. While approximate cache hits alleviate DL inference workload and increase the system throughput, they however introduce an approximation error. We analytically model our caching system performance for classic LRU and ideal caches, we perform a trace-driven evaluation of the expected performance, and we compare the benefits of our proposed approach with the state-of-the-art similarity caching.
  arXiv  Detail & Related papers  (2021-12-13T13:49:11Z)
• Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching [91.50631418179331]
  A privacy-preserving distributed deep policy gradient (P2D3PG) is proposed to maximize the cache hit rates of devices in the MEC networks. We convert the distributed optimizations into model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction.
  arXiv  Detail & Related papers  (2021-10-20T02:48:27Z)
• Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning [10.930268276150262]
  Edge caching will play a critical role in facilitating the emerging content-rich applications. It faces many new challenges, in particular, the highly dynamic content popularity and the heterogeneous caching computation. We propose Cocktail Edge Caching, that tackles the dynamic popularity and heterogeneity through ensemble learning.
  arXiv  Detail & Related papers  (2021-01-14T21:59:04Z)
• A Survey of Deep Learning for Data Caching in Edge Network [1.9798034349981157]
  This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning.
  arXiv  Detail & Related papers  (2020-08-17T12:02:32Z)
• Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks [87.6031308969681]
  This article investigates the cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA) We formulate the long-term caching placement and resource allocation optimization problem for content delivery delay minimization as a Markov decision process (MDP) We propose a Q-learning based caching placement and resource allocation algorithm, where the UAV learns and selects action with emphsoft $varepsilon$-greedy strategy to search for the optimal match between actions and states.
  arXiv  Detail & Related papers  (2020-08-12T08:33:51Z)
• A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission [98.47527781626161]
  We study a resource allocation problem for joint caching and transmission in small cell networks. We then formulate the problem as to select a file from the cache together with a transmission power level for every broadcast round. In contrast to the state-of-the-art research, the proposed approach is especially suitable for networks with time-variant statistical properties.
  arXiv  Detail & Related papers  (2020-04-13T09:07:17Z)
• PA-Cache: Evolving Learning-Based Popularity-Aware Content Caching in Edge Networks [14.939950326112045]
  We propose an evolving learning-based content caching policy, named PA-Cache in edge networks. It adaptively learns time-varying content popularity and determines which contents should be replaced when the cache is full. We extensively evaluate the performance of our proposed PA-Cache on real-world traces from a large online video-on-demand service provider.
  arXiv  Detail & Related papers  (2020-02-20T15:38:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.