A heuristic survivable virtual network mapping algorithm

Zheng, Xiangwei; Tian, Jie; Xiao, Xiancui; Cui, Xinchun; Yu, Xiaomei

doi:10.1007/s00500-018-3152-7

Cited by 39 publications

(17 citation statements)

References 24 publications

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“…In the concise approach, the VNE algorithms use as many substrate resources as required to assign virtual networks to the substrate network [8]. However, in the redundant approach, the VNE algorithms reserve extra resources for virtual networks in case some substrate nodes may fail [7,28,29]. 3.…”

Section: Related Workmentioning

confidence: 99%

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Distributed Virtual Network Embedding for Software-Defined Networks Using Multiagent Systems

2021

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Section: Related Workmentioning

confidence: 99%

“…So far, many algorithms have been proposed to solve the virtual network assignment problem [7][8][9]. The virtual network embedding algorithms may be implemented in a centralized manner or a distributed manner.…”

Section: Introductionmentioning

confidence: 99%

Distributed Virtual Network Embedding for Software-Defined Networks Using Multiagent Systems

2021

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“…As an important technology of information networks, VN allocation technology includes two specific types of resource allocation methods: static resource allocation and dynamic resource allocation. The typical static network allocation method allocates users sufficient resources to meet their peak requirement, ignoring the limited number of resources or other issues [20][21][22][23]. There are many literatures that used different strategies to improve resource utilization, including sub-graph isomorphism [24], path splitting and migration [25], topology-aware node ranking [26] or other heuristics [27][28][29].…”

Section: Related Workmentioning

confidence: 99%

A Virtual Network Resource Allocation Model Based on Dynamic Resource Pricing

et al. 2020

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The key to network virtualization technology is virtual network mapping, which has been proven to be an NP-hard problem. At present, the methods to solve the problem of virtual network mapping still have the following defects. Most of the existing literature is limited to static virtual network (VN) mapping and static linear resource pricing, which rely on peak allocation and don't meet the user dynamic resource requirements. Therefore, this paper proposes a virtual network resource allocation model based on dynamic resource pricing named GSO-RBFDM. Firstly, group search optimization (GSO) is used to optimize the node mapping scheme during the network mapping process to reduce the cost of network mapping. Secondly, a dynamic nonlinear resource pricing model is established, and genetic algorithm (GA) is used to more accurately search a low-cost network mapping path instead of the traditional Dijkstra algorithm. Finally, virtual network dynamic modeling is performed according to the user dynamic resource requirements, and radial basis function (RBF) is used to predict resource requirements to realize the dynamic resource allocation to users. Simulation results show that, compared with traditional virtual network mapping algorithms, GSO-RBFDM can not only realize dynamic resource allocation, but also show good performance in terms of acceptance rate, network cost, link pressure and average network revenue.

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“…We compare the proposed algorithm GAL with three best performance algorithms, which solve the VONs mapping problem, denoted by CAN-A, LCSD and GRC-SVNE, respectively. CAN-A, LCSD and GRC-SVNE denote the algorithms of proposed in literature [4], literature [6] and literature [50], respectively. CAN-A algorithm lies in constructing the candidate substrate node subset and the candidate substrate path subset before embedding.…”

Section: A Parameters Settingmentioning

confidence: 99%

A New Bi-Level Mathematical Model and Algorithm for VONs Mapping Problem

et al. 2020

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Elastic optical networks (EONs) virtualization can allow the virtual optical networks (VONs) to utilize all the physical resources of EONs, and can attain a high performance of the networks. However, the optimal scheme for VONs mapping should be determined. To tackle these challenges, a bi-level mathematical model is established. leader's and follower's objectives are to minimize energy consumption and the maximum index of used frequency slots, respectively. The bi-level mathematical model can determine the optimal schemes of VONs mapping. To solve the mathematical model effectively, a uniform design method is applied to generate initial population for the lower level problem. In addition, To solve the whole model effectively, a tailor-made encoding, population initialization, genetic operators and local search operator are designed. An efficient genetic algorithm with local search operator is proposed for the bilevel mathematical model. To evaluate the mathematical model and the designed algorithm, a large number of experiments are performed on three kinds of the widely used networks, and the experimental results indicate that the effectiveness of the proposed bi-level mathematical model and designed algorithms.

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A heuristic survivable virtual network mapping algorithm

Cited by 39 publications

References 24 publications

Distributed Virtual Network Embedding for Software-Defined Networks Using Multiagent Systems

Distributed Virtual Network Embedding for Software-Defined Networks Using Multiagent Systems

A Virtual Network Resource Allocation Model Based on Dynamic Resource Pricing

A New Bi-Level Mathematical Model and Algorithm for VONs Mapping Problem

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