Deep Multi‐Agent Reinforcement Learning for Cooperative Edge Caching

Gursoy, M. Cenk; Zhong, Chen; Velipasalar, Senem

doi:10.1002/9781119562306.ch21

Cited by 7 publications

(6 citation statements)

References 32 publications

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“…The mathematical model in (11) is solved using the outer approximation algorithm (OAA) followed by a heuristic approach. OAA is a conventional optimization algorithm while a heuristic algorithm is developed on basis of the best SNR.…”

Section: Proposed Algorithmsmentioning

confidence: 99%

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Bulletin of the Polish Academy of Sciences: Technical Sciences

Basir,

Chughtai,

Ali

et al. 2022

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Fog networks facilitate ultra-low latency through the use of data availability near the network edge in fog servers. Existing work in fog networks considers the objective of energy efficiency and low latency for internet-of-things (IoT) for resource allocation. These works provide solutions to energy efficiency and low latency resource allocation problem without consideration of secure communication. This article investigates the benefits of fog architecture from the perspective of three promising technologies namely device-to-device (D2D) communication, caching, and physical layer security. We propose security provisioning followed by mode selection for D2D-assisted fog networks. The secrecy rate maximization problem is formulated first, which belongs to mixed-integer nonlinear programming (MINLP) problem. It is NP-hard, that is why an exhaustive search for finding the solution is complex. Keeping in view the complexity, a nonlinear technique namely outer approximation algorithm (OAA) is applied. OAA is a traditional algorithm, whose results are compared with the proposed heuristic algorithm, namely the security heuristic algorithm (SHA). Performance of the network is observed for the different numbers of eavesdroppers, IoT nodes, and fog nodes.

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Section: Proposed Algorithmsmentioning

confidence: 99%

“…The size of cache storage significantly affects the performance of the network in terms of latency. More cached content available in the near vicinity of users results in very fewer delays [9][10][11].…”

mentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Basir,

Chughtai,

Ali

et al. 2022

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“…Motivated by recent research results, blockchain technology can be introduced into the MEC system to support many management and security services in mobile edge computing. Also, DRL-based video transmission strategy in the MEC environment is extensively studied recently [37][38][39][40][41][42][43].…”

Section: Motivationmentioning

confidence: 99%

Blockchain-Enabled Intelligent Video Caching and Transcoding in Clustered MEC Networks

Wan

2021

Security and Communication Networks

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In recent years, the number of smart devices has exploded, leading to an unprecedented increase in demand for video live and video-on-demand (VoD) services. Also, the privacy of video providers and requesters and the security of requested video data are much more threatened. In order to solve these issues, in this paper, a blockchain-enabled CMEC video transmission model (Bl-CMEC) for intelligent video caching and transcoding will be proposed to ensure the transactions’ transparency, system security, user information privacy, and integrity of the video data, enhance the ability of severs in actively caching popular video content in the CMEC system, and realize transcoding function at network edge nodes. Furthermore, we chose a scheme based on deep reinforcement learning (DRL) to intelligently access the intracluster joint caching and transcoding decisions. Then, the joint video caching and transcoding decision smart contract is specially designed to automatically manage the transaction process of the joint caching and transcoding service, which records key information of joint caching and transcoding transactions and payment information on a continuous blockchain. The simulation results demonstrate that the proposed Bl-CMEC framework not only can provide users with better QoE performance for video streaming service but also can ensure the security, integrity, and consistency for the video providers, video requesters, and video data.

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“…They proposed deep actor-critic reinforcement learning-based policies for both centralized and decentralized content caching, aiming at maximizing the cache hit rate in centralized edge caching and the cache hit rate and transmission delay as performance metrics in decentralized edge caching. Gursoy et al [52] designed a deep actor-critic RL-based multiagent framework for the edge caching problem in both a multicell network and a single-cell network with D2D communication.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Collaborative Video Caching and Transcoding in Clustered and Intelligent Edge B5G Networks

Wan

2020

Wireless Communications and Mobile Computing

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In the next-generation wireless communications system of Beyond 5G networks, video streaming services have held a surprising proportion of the whole network traffic. Furthermore, the user preference and demand towards a specific video might be different because of the heterogeneity of users’ processing capabilities and the variation of network condition. Thus, it is a complicated decision problem with high-dimensional state spaces to choose appropriate quality videos according to users’ actual network condition. To address this issue, in this paper, a Content Distribution Network and Cluster-based Mobile Edge Computing framework has been proposed to enhance the ability of caching and computing and promote the collaboration among edge severs. Then, we develop a novel deep reinforcement learning-based framework to automatically obtain the intracluster collaborative caching and transcoding decisions, which are executed based on video popularity, user requirement prediction, and abilities of edge servers. Simulation results demonstrate that the quality of video streaming service can be significantly improved by using the designed deep reinforcement learning-based algorithm with less backhaul consumption and processing costs.

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Deep Multi‐Agent Reinforcement Learning for Cooperative Edge Caching

Cited by 7 publications

References 32 publications

Bulletin of the Polish Academy of Sciences: Technical Sciences

Bulletin of the Polish Academy of Sciences: Technical Sciences

Blockchain-Enabled Intelligent Video Caching and Transcoding in Clustered MEC Networks

Deep Reinforcement Learning-Based Collaborative Video Caching and Transcoding in Clustered and Intelligent Edge B5G Networks

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