A robust control-theory-based exploration strategy in deep reinforcement learning for virtual network embedding

Dandachi, Ghina; Cerf, Sophie; Hadjadj‐Aoul, Yassine; Outtagarts, Abdelkader; Rutten, Éric

doi:10.1016/j.comnet.2022.109366

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…This comparative analysis evaluates the proposed NS‐AO algorithm against five state‐of‐the‐art DRL approaches (DQN, 32 DDQN, 31 D3QN, DPG, and DDPG) using value and policy‐based methods. The analysis provides a more comprehensive comparison of NS‐AO's relative strengths and weaknesses across various performance metrics by including these established benchmarks.…”

Section: Discussionmentioning

confidence: 99%

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DRL‐based customised resource allocation for sub‐slices in 6G network slicing

2024

Trans Emerging Tel Tech

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Abstract6G network services demand significant computer resources. Network slicing offers a potential solution by enabling customized services on shared infrastructure. However, dynamic service needs in heterogeneous environments pose challenges to resource provisioning. 6G applications like extended reality and connected vehicles require service differentiation for optimal quality of experience (QoE). Granular resource allocation within slices is a complex issue. To address the complexity of QoE services in dynamic slicing, a deep reinforcement learning (DRL) approach called customized sub‐slicing is proposed. This approach involves splitting access, transport, and core slices into sub‐slices to handle service differentiation among 6G applications. The focus is on creating sub‐slices and dynamically scaling slices for intelligent resource allocation and reallocation based on QoS requirements for each sub‐slice. The problem is formulated as an integer linear programming (ILP) optimization problem with real‐world constraints. To effectively allocate sub‐slices and dynamically scale resources, the Advantage Actor‐Critic (A2C)‐based Network Sub‐slice Allocation and Optimization (NS‐AO) algorithm is proposed. Experimental results demonstrate that the proposed algorithm outperforms the state of the art in terms of training stability, learning time, sub‐slice acceptance rate, and resilience to topology changes.

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Section: Discussionmentioning

confidence: 99%

“…They proposed a DDQN‐based algorithm to address this issue efficiently. Dandachi et al 32 proposed a self‐adaptive learning‐based optimal scheduling method for dynamic virtual networks. They used a DRL strategy called DQN, along with Monte Carlo (MC) simulations, to embed a virtual network in mobile networks.…”

Section: Related Workmentioning

confidence: 99%

DRL‐based customised resource allocation for sub‐slices in 6G network slicing

2024

Trans Emerging Tel Tech

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“…Using algorithms such as Deep Reinforcement Learning (DRL) combined with other strategies [85], [86], it participates to reduce learning cost time, computing resources, and sensitive to changes in the network. The autoscaling orchestrator allows adjusting the number of virtual network functions (i.e., CNFs) in a telecom cloud platform corresponding to the devices demand (e.g., cobots in this case).…”

Section: ) Enablers For Multi-platform Resource Orchestrationmentioning

confidence: 99%

Design Methodology for 6G End-to-End System: Hexa-X-II Perspective

Kerboeuf,

Porambage,

Jain

et al. 2024

IEEE Open J. Commun. Soc.

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As the fifth generation (5G) mobile communication systems are commercially deployed, they bring new services, enhance user experiences, and offer various opportunities to different industries. Despite its advancements, 5G encounters several challenges. To tackle these issues, global industrial, academic, and standards organizations are actively researching on sixth generation (6G) wireless communication systems. 6G networks are envisioned as a transformative shift in the interactions between the physical, digital, and human realms, paving the way for a pervasive human-centered cyber-physical world. 6G is positioned to be a platform that offers communication and beyond communication services considering both performance and value centric technological development approaches. In this paper, we present the way forward towards the design of 6G end-to-end (E2E) system as a consolidated view of leading industry stakeholders and academia in the European level 6G flagship project Hexa-X-II. We discuss the key challenges with 5G and how 6G is expected to tackle those by introducing new technological innovations and supporting novel use case requirements. We provide a comprehensive methodology for the design of a 6G E2E system including ten principles, a blueprint, and a structured design process. The architecture design principles prioritize environmental sustainability, digital inclusiveness, and trustworthiness, considering their impact on the 6G E2E system. The blueprint is described corresponding to the infrastructure, network centric application, and application layers, as well as the pervasive functionalities and the relevant technological innovations. Following the design principles and the system blueprint, the design process is demonstrated as two-way approaches (i.e., 1) key performance and value indicators based design process. 2) top-down versus bottomup alignment process) through the lens of a collaborative robot use case. Through this use case, a special attention is given to the technological enablers that cover management and orchestration functionalities and their 6G enhancement to go beyond the limitations characterizing the previous network generation. In addition, virtual modelling aspects related to digital twining and simulations for 6G E2E system design are also discussed. INDEX TERMS 6G, end-to-end, platform, system, blueprint, network architecture, design process, use case, key value indicators, key performance indicators, collaborative robots.

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NORD: NOde Ranking-based efficient virtual network embedding over single Domain substrate networks

Addya

Satpathy

et al. 2023

Computer Networks

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A robust control-theory-based exploration strategy in deep reinforcement learning for virtual network embedding

Cited by 6 publications

References 24 publications

DRL‐based customised resource allocation for sub‐slices in 6G network slicing

DRL‐based customised resource allocation for sub‐slices in 6G network slicing

Design Methodology for 6G End-to-End System: Hexa-X-II Perspective

NORD: NOde Ranking-based efficient virtual network embedding over single Domain substrate networks

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