Entropy-based Reinforcement Learning for computation offloading service in software-defined multi-access edge computing

Li, Kexin; Wang, Xingwei; Ni, Qiang; Huang, Min

doi:10.1016/j.future.2022.06.002

Cited by 15 publications

(6 citation statements)

References 35 publications

(40 reference statements)

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“…The UEs are employed dynamically; they have different mobility profiles 1 . Let

U

be the UE, and

n

be the total number of UEs.…”

Section: Zero Trust Security Model For Mecmentioning

confidence: 99%

“…Multi‐access edge computing (MEC) is a software‐defined architecture for a system that provides computation and storage at the network edge with implicit trust between MEC components that presents an open door for threat actors 1 …”

Section: Introductionmentioning

confidence: 99%

“…Multi-access edge computing (MEC) is a software-defined architecture for a system that provides computation and storage at the network edge with implicit trust between MEC components that presents an open door for threat actors. 1 The GSM Association 2 defines the MEC's technical requirements, interface properties and functional blocks. MEC-related review and survey papers [3][4][5] cover characteristics, enabling components, framework, use cases, implementation, standardization, and functional layers.…”

Section: Introductionmentioning

confidence: 99%

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Trust‐aware task load balancing in multi‐access edge computing based on blockchain and a zero trust security capability framework

Ali,

Gregory,

2023

Trans Emerging Tel Tech

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The need for low‐latency service support at the network edge has increased due to new applications such as autonomous vehicles, facial recognition, and augmented reality. Multi‐access edge computing (MEC) has emerged as a promising component of next‐generation 5G and 6G networks that provides additional storage and computing capacity at the network edge to enhance application performance, load balancing, and reduce latency. Security challenges remain, and a zero trust security framework has the potential to reduce risk and improve visibility. A zero trust management module is introduced to authenticate users after a successful registration. This article presents a three‐layer MEC model implementing a trust‐aware load balancing decision process. A Q‐learning algorithm is used to estimate trust values. Task trust and indirect trust relationships are recorded using blockchain technology. Blockchain will enable a transparent and secure mechanism that contributes to secure resource management in an MEC environment. The results provided by a framework simulation highlight the improved security outcome.

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“…The UEs are employed dynamically; they have different mobility profiles 1 . Let

U

be the UE, and

n

be the total number of UEs.…”

Section: Zero Trust Security Model For Mecmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Trust‐aware task load balancing in multi‐access edge computing based on blockchain and a zero trust security capability framework

Ali,

Gregory,

2023

Trans Emerging Tel Tech

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show abstract

“…Their article summarized the developments in value-based 3 and policy-based 4 DRL algorithms and maximal entropy-based 1 DRL developments. 5 The future issues that DRL will investigate were also evaluated and addressed. 1 The Q-learning 6 technique is a RL algorithm that is based on the Markov decision process (MDP).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Their essay detailedly reviewed the principal study topics, essential ideas, and key algorithms concerning DRL. Their article summarized the developments in value‐based 3 and policy‐based 4 DRL algorithms and maximal entropy‐based 1 DRL developments 5 . The future issues that DRL will investigate were also evaluated and addressed 1 …”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent multimedia content delivery in 5G/6G networks: A reinforcement learning approach

Iqbal

Farhan

Ullah

et al. 2023

Trans Emerging Tel Tech

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Multimedia content in 5G/6G networks makes safe, confidential, and efficient content delivery difficult. Intelligent systems that adapt to the ever‐changing network environment are needed to distribute multimedia content in these networks. Reinforcement learning (RL) can optimize multimedia content distribution based on network congestion, capacity, and user preferences. This study proposes RL‐based intelligent multimedia content distribution. RL algorithms learn from the network environment and generate optimum judgments incorporating several aspects of the suggested framework. The framework delivers multimedia material securely and privately with great quality. This study provides an intelligent multimedia content delivery architecture that uses RL approaches to solve 5G/6G content delivery problems. This research presents an RL system optimized with the double DQN algorithm having a reward of 51604.93 in 7000 episodes for efficient video file sharing on intracity buses. The RL agent balances network congestion and bandwidth by leveraging multiple sources such as bus and intersection caches and base stations, improving secure multimedia content delivery in 5G/6G networks and enhancing the passenger experience. The study confirms the system's effectiveness using reward and loss metrics and identifies potential future research directions. Future work could explore additional RL algorithms, scalability for larger networks, complex delivery scenarios, and integration with blockchain and edge computing for improved security and efficiency in multimedia content delivery.

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The Integration of Software Defined Network in Mobile Edge Computing for Task Offloading and Resource Allocation of IoT Applications

Cherhabil,

Sedrati,

Bendib

2023

Lecture Notes in Networks and Systems

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Entropy-based Reinforcement Learning for computation offloading service in software-defined multi-access edge computing

Cited by 15 publications

References 35 publications

Trust‐aware task load balancing in multi‐access edge computing based on blockchain and a zero trust security capability framework

Trust‐aware task load balancing in multi‐access edge computing based on blockchain and a zero trust security capability framework

Intelligent multimedia content delivery in 5G/6G networks: A reinforcement learning approach

The Integration of Software Defined Network in Mobile Edge Computing for Task Offloading and Resource Allocation of IoT Applications

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