Online Virtual Network Embedding Based on Virtual Links’ Rate Requirements

Aguilar-Fuster, Christian; Zangiabady, Mahboobeh; Zapata-Lara, Jose; Rubio‐Loyola, Javier

doi:10.1109/tnsm.2018.2873923

Cited by 14 publications

(12 citation statements)

References 27 publications

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“…In this section, we analyze the performance of the proposed algorithm in terms of the VNR acceptance ratio, long-term average R/C ratio, substrate (1) Calculate the node-ranking value S k of virtual nodes according to equation (35) (2) for all unembedded virtual nodes in VNR (3) Select the virtual node n k with the highest node-ranking value S k (4) Rank the virtual nodes using breadth first search (BFS) algorithm and put the ranking results into Virtual_node_list1 (5) end for (6) for each unembedded virtual node in Virtual_node_list1 (7) Filter the set of substrate nodes that meet the resource and location constraints of n k (8) Calculate the node-ranking value S k of substrate nodes according to equation (35) (9) Rank the substrate nodes by S k in decreasing order (10) Embed the virtual node onto the substrate node with the highest S k (11) end for ALGORITHM 1: VNE-NRTD-S.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…It generated virtual network identifiers by combining the link-grained labels with their location information. Since the virtual links are embedded onto one or more substrate links that may be connected through intermediate substrate nodes, the resource required by the intermediate substrate node to forward the data traffic is also considered in [35]. Zhao et al [36] focused on embedding the virtual networks onto the distributed soft-defined network with multiple controllers.…”

Section: Heuristic Algorithmsmentioning

confidence: 99%

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Novel Node-Ranking Approach for SDN-Based Virtual Network Embedding

Shi

Meng

Kang

et al. 2020

Mathematical Problems in Engineering

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Network virtualization is considered as a key technology for the future network. The emergence of software-defined network (SDN) provides a platform for the research and development of network virtualization. One of the key challenges in network virtualization is virtual network embedding (VNE). Some of the previous VNE algorithms perform virtual node embedding, which combines the nodes’ resource attributes and local topology attributes by arithmetic operations. On the one hand, it is not easy to distinguish the topological differences between SN and VN only by simple topology metrics. On the other hand, it is easy to ignore the different weight impacts of different metrics using only arithmetic operations, which will lead to an unbalanced embedding solution. To deal with these issues, we propose a novel node-ranking approach based on topology-differentiating (VNE-NRTD) for SDN-based virtual network embedding. Owing to the topological difference between SN and VN, different node metrics are used to quantify the substrate nodes and virtual nodes, respectively. Then, the nodes are ranked using the modified set pair analysis (SPA) method to avoid the unbalanced embedding solution. On this basis, we introduce the global bandwidth of the network topology into node-ranking to further improve the efficiency of node embedding. The simulation results show that the VNE-NRTD algorithm proposed in this paper outperforms other latest heuristic algorithms in terms of the VNR acceptance ratio, long-term average R/C ratio, substrate node utilization, and substrate link utilization.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Heuristic Algorithmsmentioning

confidence: 99%

Novel Node-Ranking Approach for SDN-Based Virtual Network Embedding

Shi

Meng

Kang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…They proposed a set of algorithms that involved several generalized distance metrics called DMEA-X and DMEA-2D, pursuing a trade-off between computation time and solution quality. These proposed algorithms strove to surpass the impressive embedding performance of DViNE-SP 7 while exceeding the fast runtime of GAR-SP in Yu et al 4 The problem of CPU capacity of Intermediate Substrate Nodes (INSs) was raised in Aguilar-Fuster et al 23 to forward traffic on virtual links at different required rates. This obstacle had been mostly overlooked in the literature.…”

Section: Related Workmentioning

confidence: 99%

Distributed parallel genetic algorithm for online virtual network embedding

Nguyen

Huang

2020

Int J Communication

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Network virtualization (NV) has emerged as a promising paradigm to address the constraints of implementing new protocols and services in existing network architecture by allowing the simultaneous coexistence of multiple heterogeneous virtual networks on a shared substrate infrastructure. Hence, NV is a critical technology for establishing future network architectures (e.g., 5Gnetwork and the smart Internet of Things [IoT]). Virtual network embedding (VNE) is a major challenge in NV since it is acknowledged as N P-hard. Many VNE solutions have been proposed over the past decade. However, the proposed solutions merely centralize VNE node mapping while recommending virtual link embedding for the shortest path method or multicommodity flow (MCF) mechanism. This research paper presents an intelligent virtual network orchestration based on genetic algorithm (GA) for the link mapping stage that implements distributed parallelism to significantly and efficiently reduce the operation time. Our extensive simulations have demonstrated that the proposed algorithm not only outperforms the state-of-the-art VNE algorithm in all performance metrics but also achieves 44.01% faster embedding speed than the most well-known, fastest link mapping method in VNE.

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“…is the VNE problem [14]- [16]. Most of the existing VNE algorithms are designed based on heuristic methods, which have the following limitations: VNE tends to converge to the local optimal solution and cannot obtain the global optimal solution; The heuristic approach relies on manually formulating a set of rules and assumptions that do not fully reproduce the reality of substrate network and virtual network and the connections between them [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Network Resource Allocation Strategy Based on Deep Reinforcement Learning

Zhang¹,

Wang

Zhang

et al. 2020

IEEE Open J. Comput. Soc.

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The traditional Internet has encountered a bottleneck in allocating network resources for emerging technology needs. Network virtualization (NV) technology as a future network architecture, the virtual network embedding (VNE) algorithm it supports shows great potential in solving resource allocation problems. Combined with the efficient machine learning (ML) algorithm, a neural network model close to the substrate network environment is constructed to train the reinforcement learning agent. This paper proposes a two-stage VNE algorithm based on deep reinforcement learning (DRL) (TS-DRL-VNE) for the problem that the mapping result of existing heuristic algorithm is easy to converge to the local optimal solution. For the problem that the existing VNE algorithm based on ML often ignores the importance of substrate network representation and training mode, a DRL VNE algorithm based on full attribute matrix (FAM-DRL-VNE) is proposed. In view of the problem that the existing VNE algorithm often ignores the underlying resource changes between virtual network requests, a DRL VNE algorithm based on matrix perturbation theory (MPT-DRL-VNE) is proposed. Experimental results show that the above algorithm is superior to other algorithms.

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Online Virtual Network Embedding Based on Virtual Links’ Rate Requirements

Cited by 14 publications

References 27 publications

Novel Node-Ranking Approach for SDN-Based Virtual Network Embedding

Novel Node-Ranking Approach for SDN-Based Virtual Network Embedding

Distributed parallel genetic algorithm for online virtual network embedding

Network Resource Allocation Strategy Based on Deep Reinforcement Learning

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