A unified enhanced particle swarm optimization‐based virtual network embedding algorithm

Zhang, Zhongbao; Cheng, Xiang; Su, Sen; Wang, Yiwen; Shuang, Kai; Luo, Yan

doi:10.1002/dac.1399

Cited by 123 publications

(146 citation statements)

References 21 publications

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“…Considering this algorithm as metaheuristic algorithm make it more applicable in large search space. PSO is optimization algorithm aims to find the near optimal solution among other candidate ones [10], [11]. In PSO, each candidate solution considered as particle, where each particle has its own position "x" and velocity "v".…”

Section: B Particle Swarm Optimizationmentioning

confidence: 99%

“…represent server's baseline power and . Where, represent energy consumed when the server reaches its highest utilization [10] [19].…”

Section: B Energy Consumption Modelmentioning

confidence: 99%

“…PSO is a population based algorithm that starts with candidate solutions in one iteration, and improve them in the next one [10]. The performance of the proposed algorithm was tested against existing algorithms.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: B Particle Swarm Optimizationmentioning

confidence: 99%

“…represent server's baseline power and . Where, represent energy consumed when the server reaches its highest utilization [10] [19].…”

Section: B Energy Consumption Modelmentioning

confidence: 99%

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Energy-Aware Virtual Network Embedding Approach for Distributed Cloud

Alzahrani¹,

Shahin²

2017

ijacsa

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“…The proposed algorithm allows more the one virtual node to be coexist in the same substrate node to leverage the advantages of inter ram switch techniques. Wang et al (2013) proposed VNE algorithm similar to the proposed algorithm in Zhang et al (2013) proposed two VN embedding models: an integer linear programming model and a mixed integerprogramming model. To solve these models, Zhang et al proposed a discrete particle swarm optimization based VNE algorithm, called RW-PSO, to find a near optimal VNE solutions.…”

Section: Related Workmentioning

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%

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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Virtual network embedding with pre‐transformation and incentive convergence mechanism

Wang

Peng

et al. 2016

Concurrency and Computation

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Summary Efficient and fair resource allocation for multitudinous virtual networks running cloud‐based applications is crucial to archive dynamic resources multi‐tenancy in cloud computing. In order to solve the problem, we propose a novel virtual network embedding (VNE) algorithm to increase revenue and utilization of substrate network as well as to improve acceptance fairness of virtual networks. First, we present a virtual topology pre‐transformation mechanism leveraging reusable technology to reduce topology difference and achieve acceptance fairness. Then, because of the Non‐deterministic polynomial‐time (NP)‐hard characteristics of VNE, we model the problem as an integer linear programming problem and solve the VNE problem with a discrete particle swarm optimization‐based algorithm. The operations and parameters of particles are well redefined according to the VNE context. Finally, an incentive convergence mechanism is proposed to reduce mapping complexity, which can be used to accelerate convergence and to save more bandwidth by exploiting individual candidate nodes' lists. Simulation results prove that our proposed method is superior to the existing similar algorithms in terms of physical resource utilization, acceptance fairness, revenue/cost ratio, and searching efficiency. Copyright © 2016 John Wiley & Sons, Ltd.

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A unified enhanced particle swarm optimization‐based virtual network embedding algorithm

Cited by 123 publications

References 21 publications

Energy-Aware Virtual Network Embedding Approach for Distributed Cloud

Energy-Aware Virtual Network Embedding Approach for Distributed Cloud

Memetic Elitist Pareto Evolutionary Algorithm for Virtual Network Embedding

Virtual network embedding with pre‐transformation and incentive convergence mechanism

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