Toward Enabling Network Slice Mobility to Support 6G System

Bagaa, Miloud; Dutra, Diego Leonel Cadette; Taleb, Tarik; Flinck, Hannu

doi:10.1109/twc.2022.3182591

Cited by 10 publications

(1 citation statement)

References 29 publications

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“…6G mobile communication network will support a variety of applications, for instance immersive cloud augmented reality, autonomous driving, holographic communication, smart manufacturing and other new applications [1][2][3]. These new applications bring convenience to people's lives, but also inevitably increase mobile data traffic.…”

Section: Introductionmentioning

confidence: 99%

Epidemic dynamics edge caching strategy for 6G networks

Wang,

Zhang,

Zhang

2024

Front. Phys.

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By caching popular content on edge servers closer to users to respond to users’ content requests in 6G networks, the transmission load of backhaul links can be reduced. However, the time-varying characteristics of content prevalence leads to the issue that the cache content may not match the user’s needs, resulting in a decrease in cache success ratio. To solve these issues, we proposed a cache distribution strategy based on epidemic dynamics (CDSED) for 6G edge network. First, a 6G edge caching content model (6G ECCM) is constructed to establish the process of cache content propagation among users as an infectious disease propagation process, analyze the distribution of users’ interest in cache content and obtain the cache content state probability prediction equation, and use the cache content state probability prediction equation to predict the cache content prevalence. Second, based on the predicted prevalence results, a prevalence predictive genetic-annealing cache content algorithm (PGAC) is proposed with the optimization objective of maximizing the cache success ratio. The algorithm designs the selection function of the traditional genetic algorithm as a simulated annealing selection function based on the cache content success ratio, which avoids the defect of the genetic algorithm that converges to the locally optimum cache strategy too early and enhances the cache success ratio. Finally, the optimum cache content decision is solved by iterative alternation. Simulation results demonstrate that CDSED strategy can enhance cache success ratio than the LRU strategy, the LFU strategy, and the MPC strategy.

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Section: Introductionmentioning

confidence: 99%

Epidemic dynamics edge caching strategy for 6G networks

Wang,

Zhang,

Zhang

2024

Front. Phys.

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Dynamic and efficient resource allocation for 5G end‐to‐end network slicing: A multi‐agent deep reinforcement learning approach

Asim Ejaz,

Wu,

Iqbal

2024

Int J Communication

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SummaryThe rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end‐users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL‐based approach outperforms other state‐of‐the‐art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively.

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