Using Heavy Clique Base Coarsening to Enhance Virtual Network Embedding

Shahin, Ashraf A.

doi:10.14569/ijacsa.2015.060118

Cited by 3 publications

(5 citation statements)

References 25 publications

(37 reference statements)

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“…The second algorithm coarsens VNs using Heavy Edge Matching (HEM) technique to minimize the total required bandwidth. In (Shahin, 2015a), Shahin proposed another VNE technique, which coarsens virtual network using Heavy Clique matching technique and optimizes the coarsened virtual networks using a refined Kernighan-Lin algorithm. Zhu and Ammar (2006) proposed two VNE algorithms to find exact VNE solutions.…”

Section: Related Workmentioning

confidence: 99%

“…Substrate network (SN): as in our previous work (Shahin, 2015a;Shahin, 2015b), SN is modeled as a graph 7 8 = 9 8 , : 8 , which is a weighted undirected graph. 9 8 is the set of all substrate nodes and : 8 is the set of all substrate links.…”

Section: Virtual Network Embedding Model and Problem Formulationmentioning

confidence: 99%

“…The set : < = contains all virtual links and each virtual link is weighted by required bandwidth. Figure 1( (Shahin, 2015a;Shahin, 2015b;Cheng et al, 2011;Zhang et al, 2013), the revenue of embedding > ? at time A is the sum of all accommodated virtual resources of the virtual network request > ?…”

Section: Virtual Network Embedding Model and Problem Formulationmentioning

confidence: 99%

“…Virtual Network Embedding Cost: as in (Shahin, 2015a;Shahin, 2015b), the cost of embedding > ? at time A is defined as total allocated substrate resources to > ?…”

Section: Virtual Network Embedding Model and Problem Formulationmentioning

confidence: 99%

“…To evaluate the proposed MEPDE-VNE algorithm, we compared its performance with the following algorithms: RW-MaxMatch (Cheng et al, 2011), RW-BFS (Cheng et al, 2011), BFSN-HEM (Shahin, 2015b), vnmFlib (Lischka & Karl, 2009), HCM (Shahin, 2015a), and BFSN (Shahin, 2015b).…”

Section: Performance Evaluationmentioning

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: Related Workmentioning

confidence: 99%

Section: Virtual Network Embedding Model and Problem Formulationmentioning

confidence: 99%

Section: Virtual Network Embedding Model and Problem Formulationmentioning

confidence: 99%

“…Virtual Network Embedding Cost: as in (Shahin, 2015a;Shahin, 2015b), the cost of embedding > ? at time A is defined as total allocated substrate resources to > ?…”

Section: Virtual Network Embedding Model and Problem Formulationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding

Shahin¹

2015

CIS

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Memetic Multi-Objective Particle Swarm Optimization-Based Energy-Aware Virtual Network Embedding

Shahin¹

2015

ijacsa

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In cloud infrastructure, accommodating multiple virtual networks on a single physical network reduces power consumed by physical resources and minimizes cost of operating cloud data centers. However, mapping multiple virtual network resources to physical network components, called virtual network embedding (VNE), is known to be NP-hard. With considering energy efficiency, the problem becomes more complicated. In this paper, we model energy-aware virtual network embedding, devise metrics for evaluating performance of energy aware virtual network-embedding algorithms, and propose an energy aware virtual network-embedding algorithm based on multi-objective particle swarm optimization augmented with local search to speed up convergence of the proposed algorithm and improve solutions quality. 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 revenue and decreasing energy consumption.

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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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Using Heavy Clique Base Coarsening to Enhance Virtual Network Embedding

Cited by 3 publications

References 25 publications

Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding

Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding

Memetic Multi-Objective Particle Swarm Optimization-Based Energy-Aware Virtual Network Embedding

Energy-Aware Virtual Network Embedding Approach for Distributed Cloud

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