Virtual network embedding through topology awareness and optimization

Cheng, Xiang; Su, Sen; Zhang, Zhongbao; Shuang, Kai; Yang, Fangchun; Luo, Yan; Wang, Jie

doi:10.1016/j.comnet.2012.01.022

Cited by 120 publications

(129 citation statements)

References 24 publications

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“…MEPDE-VNE algorithm increases number of accepted virtual network requests. Although, VNR acceptance ratio defined by equation 5 was used by many researchers to evaluate performance (Cheng et al, 2011;Cheng et al, 2012), it does not consider the VNR resource size. For example, in figure 4, algorithm vnmFlib accepted around 29% of the VNRs but it accepted only around 13% of the total VNR resources as shown in figure 6.…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…Zhang et al used RW-PSO algorithm only to propose a possible virtual node mapping solutions and mapped virtual links using shortest path algorithm and greedy k-shortest paths algorithm. Cheng et al (2012) proposed an enhanced version of the RW-PSO algorithm by combining the topology-aware node ranking measure proposed in (Cheng et al, 2011) with discrete Particle Swarm Optimization VNE algorithm. Chowdhury et al, (2009Chowdhury et al, ( ) (2012 formulated the VNE problem as a mixed integer-programming model and relaxed the proposed model to linear-programming model.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, metaheuristic-based techniques have received more attention to be used for finding near optimal solution in short execution time Cheng et al, 2012;Yuan et al, 2013;Wang et al, 2013). Genetic algorithm (GA) is one of the widely used metaheuristic-based techniques.…”

Section: Introductionmentioning

confidence: 99%

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Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding

Shahin¹

2015

CIS

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Assigning virtual network resources to physical network components, called Virtual Network Embedding, is a major challenge in cloud computing platforms. In this paper, we propose a memetic elitist pareto evolutionary algorithm for virtual network embedding problem, which is called MEPE-VNE. MEPE-VNE applies a non-dominated sortingbased multi-objective evolutionary algorithm, called NSGA-II, to reduce computational complexity of constructing a hierarchy of non-dominated Pareto fronts and assign a rank value to each virtual network embedding solution based on its dominance level and crowding distance value. Local search is applied to enhance virtual network embedding solutions and speed up convergence of the proposed algorithm. To reduce loss of good solutions, MEPE-VNE ensures elitism by passing virtual network embedding solutions with best fitness values to next generation. Performance of the proposed algorithm is evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithm improves virtual network embedding by increasing acceptance ratio and revenue while decreasing the cost incurred by substrate network.

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Section: Evaluation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding

Shahin¹

2015

CIS

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“…Their focus was on determine when and which virtual machine should be migrated, and to witch host. In [16] authors presented the algorithm to minimize the power consumption by switching off as many substrate nodes as possible without affecting the overall network performance. Additionally, they proposed Mixed Integer Program to solve VN embedding problem which considered as NP-complete.…”

Section: Related Workmentioning

confidence: 99%

“…The weight of coarsened node equal to the sum of vertices contained. The proposed algorithm follows Heavy Clique Matching (HCM) to obtain coarsen graphs [16], [20].…”

Section: ) Coarsening Vnr Phasementioning

confidence: 99%

Energy-Aware Virtual Network Embedding Approach for Distributed Cloud

Alzahrani¹,

Shahin²

2017

ijacsa

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Abstract-Network virtualization has caught the attention of many researchers in recent years. It facilitates the process of creating several virtual networks over a single physical network. Despite this advantage, however, network virtualization suffers from the problem of mapping virtual links and nodes to physical network in most efficient way. This problem is called virtual network embedding ("VNE"). Many researches have been proposed in an attempt to solve this problem, which have many optimization aspects, such as improving embedding strategies in a way that preserves energy, reducing embedding cost and increasing embedding revenue. Moreover, some researchers have extended their algorithms to be more compatible with the distributed clouds instead of a single infrastructure provider ("ISP"). This paper proposes energy aware particle swarm optimization algorithm for distributed clouds. This algorithm aims to partition each virtual network request ("VNR") to subgraphs, using the Heavy Clique Matching technique ("HCM") to generate a coarsened graph. Each coarsened node in the coarsened graph is assigned to a suitable data center ("DC"). Inside each DC, a modified particle swarm optimization algorithm is initiated to find the near optimal solution for the VNE problem. The proposed algorithm was tested and evaluated against existing algorithms using extensive simulations, which shows that the proposed algorithm outperforms other algorithms.

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Virtualization in the Cloud

Fonseca

Boutaba

2015

Cloud Services, Networking, and Management

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Virtual network embedding through topology awareness and optimization

Cited by 120 publications

References 24 publications

Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding

Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding

Energy-Aware Virtual Network Embedding Approach for Distributed Cloud

Virtualization in the Cloud

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