Reinforcement Learning-Based Optimal Computing and Caching in Mobile Edge Network

Qian, Yichen; Wang, Rui; Wu, Jun; Tan, Bin; Ren, Haoqi

doi:10.1109/jsac.2020.3000396

Cited by 61 publications

(53 citation statements)

References 25 publications

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“…Tan et al [34] have proposed a reinforcement learningbased optimal computing and caching scheme for edge net-works. The problem has been formulated as an infinitehorizon average-cost Markov decision process (MDP).…”

Section: E Predictive Caching Algorithmsmentioning

confidence: 99%

A Novel Predictive-Collaborative-Replacement (PCR) Intelligent Caching Scheme for Multi-Access Edge Computing

2021

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Multi-Access Mobile Edge Computing (MEC) is proclaimed as a key technology for reducing service processing delays in 5G networks. One of the use cases in MEC is content caching as a way of bringing resources closer to the end-users. Consequently, both latency and QoE are reduced. However, MEC has a limited storage space compared to the cloud. Therefore, there is a need to effectively manage the cache storage. This paper proposes and evaluates a novel scheme (PCR) that combines proactive prediction, collaboration among MECs and replacement algorithm to manage content caching in MEC. Results show that the proposed replacement scheme outperforms conventional baseline content caching algorithms LFU, LRU, MQ, FBR, LFRU. This has been validated with experimental results using a real dataset (MovieLens20M dataset) and comparison with contemporary Long Short Term Memory (LSTM) based caching algorithm.

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Section: E Predictive Caching Algorithmsmentioning

confidence: 99%

A Novel Predictive-Collaborative-Replacement (PCR) Intelligent Caching Scheme for Multi-Access Edge Computing

2021

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“…Two papers contain contributions to the area of Mobile Edge Networks. In "Reinforcement Learning Based Optimal Computing and Caching in Mobile Edge Network," Qian et al present a hierarchical reinforcement learning (RL) algorithm for jointly pushing and caching content in mobile edge computing (MEC) networks [10]. An infinite-horizon average-cost Markov decision process (MDP) problem is formulated to maximize bandwidth utilization and decrease the amount of data transmitted.…”

Section: The Selected Papersmentioning

confidence: 99%

“…Cache admission and eviction policies for networked environments are studied in three papers. One of them performs the investigation in the context of the mobile edge and is introduced above [10]. The second paper focuses on content delivery networks and is titled "Learning-Based Cache Admission for Content Delivery" [12].…”

Section: The Selected Papersmentioning

confidence: 99%

“…Regarding specific machine-learning methods that are introduced to address networking problems, many papers propose reinforcement learning techniques [2], [3], [7], [10]- [13], all of which, except [11], use model-free reinforcement learning. More specifically, [10], [12], and [13] apply a value-based approach, often referred to as Q-learning, while [2] and [3] follow a policy-based Approach; [7] applies a multiarmed bandit model as the basis for reinforcement learning, while all other above-referenced papers rely on Markov Decision Processes (MDPs).…”

Section: The Selected Papersmentioning

confidence: 99%

“…Many works apply deep learning methods, which are based on neural networks [2]- [5], [8]- [10], [13], [15], [16]. Some papers use methods based on Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) [5], [8].…”

Section: The Selected Papersmentioning

confidence: 99%

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Guest Editorial Special Issue on Advances in Artificial Intelligence and Machine Learning for Networking

Chemouil

Hui

Kellerer

et al. 2020

IEEE J. Select. Areas Commun.

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I. INTRODUCTION A RTIFICIAL Intelligence (AI) and Machine Learning (ML) approaches have emerged in the networking domain with great expectation. They can be broadly divided into AI/ML techniques for network engineering and management, network designs for AI/ML applications, and system concepts. AI/ML techniques for networking and management improve the way we address networking. They support efficient, rapid, and trustworthy engineering, operations, and management. As such, they meet the current interest in softwarization and network programmability that fuels the need for improved network automation in agile infrastructures, including edge and fog environments. Network design and optimization for AI/ML applications addresses the complementary topic of supporting AI/ML-based systems through novel networking techniques, including new architectures and algorithms. The third topic area is system implementation and open-source software development. This evolution draws particular attention to interdisciplinary approaches. Researchers in communication networks apply ML and AI concepts to optimize and automate network architecture, control, and management. Similarly, AI experts collaborate with networking researchers to optimize network support for architecture and design of data communication and processing for AI purposes. This special issue is a follow-up to the JSAC's Special Issue on Artificial Intelligence and Machine Learning for Networking and Communications published in June 2019 [1]. It has been organized by the same core team of researchers.

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Hierarchical Reinforcement Learning-Based Mobility-Aware Content Caching and Delivery Policy for Vehicle Networks

Zhang

Lai

Yang

2023

Algorithms and Architectures for Parallel Processing

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Reinforcement Learning-Based Optimal Computing and Caching in Mobile Edge Network

Cited by 61 publications

References 25 publications

A Novel Predictive-Collaborative-Replacement (PCR) Intelligent Caching Scheme for Multi-Access Edge Computing

A Novel Predictive-Collaborative-Replacement (PCR) Intelligent Caching Scheme for Multi-Access Edge Computing

Guest Editorial Special Issue on Advances in Artificial Intelligence and Machine Learning for Networking

Hierarchical Reinforcement Learning-Based Mobility-Aware Content Caching and Delivery Policy for Vehicle Networks

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