Virtual Network Embedding for NGSO Systems: Algorithmic Solution and SDN-Testbed Validation

Minardi, Mario; Vu, Thang X.; Lei, Lei; Politis, Christos; Chatzinotas, Symeon

doi:10.1109/tnsm.2022.3225748

Cited by 10 publications

(1 citation statement)

References 29 publications

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“…The VNR deployment cost consists of the cost of embedding new VNRs, which may involve the migration of VNF mapping for incomplete requests arrived at earlier time slots [26]. This cost depends on the number of VNFs mapped or remapped for a given service.…”

Section: Cost Modelmentioning

confidence: 99%

ReViNE: Reinforcement Learning-Based Virtual Network Embedding in Satellite-Terrestrial Networks

Maity,

Vu,

Chatzinotas

2024

IEEE Trans. Commun.

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This paper addresses the virtual network embedding (VNE) problem in integrated satellite-terrestrial networks (STNs). VNE consists of mapping virtual network functions (VNFs) in a service function chain (SFC) to physical nodes and mapping virtual links connecting the VNFs to physical links. Compared to terrestrial networks, VNE in STNs is challenging due to the movement of the non-geostationary orbit (NGSO) satellites and limited onboard processing resources. A static VNE strategy fails to efficiently address the diverse requirements of heterogeneous service requests in such a highly dynamic topology. In addition, existing solutions do not consider the capacity limitation and connectivity duration of inter-satellite links (ISLs) and ground-to-satellite links, which are essential parameters for deploying a VNE strategy in STNs. This work proposes a heuristic solution and reinforcement learning (RL)-based improved solution for the VNE scheme that can dynamically modify the existing VNF deployment strategy to maximize the average service acceptance rate and revenue. The RL agent selects a suitable VNE strategy for each service request considering the time-varying network topology and service's requirements. The proposed scheme increases the service acceptance ratio by 19.95% compared to the benchmark TS-MAPSCH.

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Section: Cost Modelmentioning

confidence: 99%

ReViNE: Reinforcement Learning-Based Virtual Network Embedding in Satellite-Terrestrial Networks

Maity,

Vu,

Chatzinotas

2024

IEEE Trans. Commun.

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Novel Initialization Functions for Metaheuristic-Based Online Virtual Network Embedding

Rubio-Loyola,

Aguilar-Fuster

2024

J Netw Syst Manage

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Virtual network embedding (VNE) is the process of allocating resources in a substrate (i.e. physical) network to support virtual networks optimally. The VNE problem is an NP-hard problem that has been studied for more than a decade in the continuous seek to maximize the revenue of physical infrastructures with more efficient VNE solutions. Metaheuristics have been widely used in online VNE as they incorporate mechanisms to avoid local optimum solutions, explore larger search spaces, and keep acceptable execution times. All metaheuristic optimization algorithms require initialization for which the vast majority of online VNE solutions implement random initialization. This paper proposes three novel initialization functions namely, Initialization Based on Node Selection (IFNS), Initialization Function Based on Community Detection (IFCD), and Initialization Function Based on Previous Solutions (IFPS), intending to enhance the performance of the online VNE process. Through simulation, our initialization functions have been proven to enhance the acceptance rate, revenue, and revenue-to-cost metrics of the VNE process. The enhancements achieved by our initialization functions are statistically significant and their implementation does not add computational overhead to the classic VNE approaches.

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DVNE-DRL: dynamic virtual network embedding algorithm based on deep reinforcement learning

Xiao

2023

Sci Rep

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Virtual network embedding (VNE), as the key challenge of network resource management technology, lies in the contradiction between online embedding decision and pursuing long-term average revenue goals. Most of the previous work ignored the dynamics in Virtual Network (VN) modeling, or could not automatically detect the complex and time-varying network state to provide a reasonable network embedding scheme. In view of this, we model a network embedding framework where the topology and resource allocation change dynamically with the number of network users and workload, and then introduce a deep reinforcement learning method to solve the VNE problem. Further, a dynamic virtual network embedding algorithm based on Deep Reinforcement Learning (DRL), named DVNE-DRL, is proposed. In DVNE-DRL, VNE is modeled as a Markov Decision Process (MDP), and then deep learning is introduced to perceive the current network state through historical data and embedded knowledge, while utilizing reinforcement learning decision-making capabilities to implement the network embedding process. In addition, we improve the method of feature extraction and matrix optimization, and consider the characteristics of virtual network and physical network together to alleviate the problem of redundancy and slow convergence. The simulation results show that compared with the existing advanced algorithms, the acceptance rate and average revenue of DVNE-DRL are increased by about 25% and 35%, respectively.

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Virtual Network Embedding for NGSO Systems: Algorithmic Solution and SDN-Testbed Validation

Cited by 10 publications

References 29 publications

ReViNE: Reinforcement Learning-Based Virtual Network Embedding in Satellite-Terrestrial Networks

ReViNE: Reinforcement Learning-Based Virtual Network Embedding in Satellite-Terrestrial Networks

Novel Initialization Functions for Metaheuristic-Based Online Virtual Network Embedding

DVNE-DRL: dynamic virtual network embedding algorithm based on deep reinforcement learning

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