Link mapping-oriented ant colony system for virtual network embedding

Zheng, Hongkun; Li, Jingjing; Gong, Yue-Jiao; Chen, Wei-Neng; Yu, Zhiwen; Zhan, Zhi-Hui; Lin, Ying

doi:10.1109/cec.2017.7969445

Cited by 16 publications

(12 citation statements)

References 22 publications

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“…Following that, virtual links are mapped to the substrate links according to the corresponding substrate nodes with the shortest or k-shortest path [10]. However, since the solution space is restricted when node mapping is executed without the consideration of its interaction with link resource restriction [14], such algorithms usually provide unsatisfactory results.…”

Section: A Typical Vne Algorithmsmentioning

confidence: 99%

“…In VNE, the mapping sequence has a positive effect on the result of the VNE algorithm. Therefore, for ranking methods of virtual nodes, Zheng et al [14] introduced a ranking score calculated by summing all the link bandwidth resource which the target node connects. With an Ant Colony System (ACS) based mapping algorithm they solved the VNE problem to improve the optimization performance and reduce the cost of time.…”

Section: B Node Ranking and Representative Algorithmsmentioning

confidence: 99%

“…Assuming that virtual nodes 1, 3, 4 and 5 has the same CPU resource request, thus their ranking indicator are same through Eq. (11)- (14), i.e. we cannot decide an accurate ranking sequence in this situation.…”

Section: A Virtual Nodes Rankingmentioning

confidence: 99%

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Topology-Oriented Virtual Network Embedding Approach for Data Centers

2019

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Currently, data centers have become an attractive candidate for users that require IT resources in the form of virtual networks to run their applications. Optimal mapping of the virtual network on the top of the substrate network with resource constraint is called virtual network embedding (VNE) problem. Most of the VNE algorithms are general algorithms for random topology and not suitable for data centers due to particular topological characteristics. To solve the VNE problem in data centers, this paper develops a topology-oriented algorithm based on the Discrete Particle Swarm Optimization (DPSO). We first develop a maximum spanning algorithm to compute the ranking of virtual nodes based on, not only its bandwidth and degree, but also its connectivity in the entire virtual network. Then, the virtual networks are embedded onto the substrate network according to the connectivity ranking result by a DPSO-based algorithm, in which we also propose a topological heuristic information of substrate network and combine it into the particle search process for boosting convergence speed and revenue/cost ratio of substrate network. The evaluation results show that the proposed algorithm can improve the optimization performance of VNE by comparing with a few existing algorithms. INDEX TERMS Data center, virtual resource allocation, virtual network embedding, discrete particle swarm optimization.

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Section: A Typical Vne Algorithmsmentioning

confidence: 99%

Section: B Node Ranking and Representative Algorithmsmentioning

confidence: 99%

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Topology-Oriented Virtual Network Embedding Approach for Data Centers

2019

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“…Different from previous work that only considers the resource of nodes in node embedding phase, they take the distance message related to links into consideration so that they can reduce the cost of VN requests. Zheng et al [13] proposed a link resource heuristic information and incorporate it into the search process of ACS. They sort the embedding sequence of virtual nodes according to their link resources.…”

Section: Introductionmentioning

confidence: 99%

Energy‐Efficient Virtual Network Embedding Algorithm Based on Hopfield Neural Network

Zhuang

Yang

et al. 2021

Wireless Communications and Mobile Computing

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To solve the energy-efficient virtual network embedding problem, this study proposes an embedding algorithm based on Hopfield neural network. An energy-efficient virtual network embedding model was established. Wavelet diffusion was performed to take the structural feature value into consideration and provide a candidate set for virtual network embedding. In addition, the Hopfield network was used in the candidate set to solve the virtual network energy-efficient embedding problem. The augmented Lagrangian multiplier method was used to transform the energy-efficient virtual network embedding constraint problem into an unconstrained problem. The resulting unconstrained problem was used as the energy function of the Hopfield network, and the network weight was iteratively trained. The energy-efficient virtual network embedding scheme was obtained when the energy function was balanced. To prove the effectiveness of the proposed algorithm, we designed two experimental environments, namely, a medium-sized scenario and a small-sized scenario. Simulation results show that the proposed algorithm achieved a superior performance and effectively decreased the energy consumption relative to the other methods in both scenarios. Furthermore, the proposed algorithm reduced the number of open nodes and open links leading to a reduction in the overall power consumption of the virtual network embedding process, while ensuring the average acceptance ratio and the average ratio of the revenue and cost.

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“…For example, imitating the foraging behavior of natural organisms led to the creation of the particle swarm optimization algorithm [15], artificial bee colony algorithm [16], and ant colony algorithm [17]. These methods tend to find feasible solutions that approximate the best options and that have been widely applied in solving the VNE problems (see our previous work [18], [19] and the related work [20]- [24]). However, the swarm intelligence algorithms in the previous studies did not completely consider the communication between the individuals in the period of individual foraging.…”

Section: Introductionmentioning

confidence: 99%

Energy-Driven Virtual Network Embedding Algorithm Based on Enhanced Bacterial Foraging Optimization

Zhuang

Tian

et al. 2020

IEEE Access

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One of the core challenges facing network virtualization is how to manage the underlying resources to host more virtual networks (VNs) with less energy. With the goal of reducing the energy consumption of virtual network embedding (VNE) and ensuring the VNE solution validity, this paper presents a new approach based on the bacterial foraging optimization (BFO) algorithm devoted to using the underlying resources more effectively and reasonably. In particular, by mimicking the information interaction between bacteria in the process of foraging, we devise a topology-aware mapping strategy, known as SBR_E, to build the candidate set of the VNE solutions. Then, we leverage SBR_E to design the energy manager of the optimization process and develop an energy-driven VNE algorithm based on BFO identified as EBFO to search for the optimal solution. Experimental results show that our method is superior to the existing algorithms in terms of reducing energy consumption and improving the VN request acceptance rate and revenue-cost ratio.

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Link mapping-oriented ant colony system for virtual network embedding

Cited by 16 publications

References 22 publications

Topology-Oriented Virtual Network Embedding Approach for Data Centers

Topology-Oriented Virtual Network Embedding Approach for Data Centers

Energy‐Efficient Virtual Network Embedding Algorithm Based on Hopfield Neural Network

Energy-Driven Virtual Network Embedding Algorithm Based on Enhanced Bacterial Foraging Optimization

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