Energy-aware virtual network embedding through consolidation

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

doi:10.1109/infcomw.2012.6193473

Cited by 55 publications

(55 citation statements)

References 20 publications

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“…The proposed algorithm focused on two stages: finding initial solutions "particles", and applying modified particle swarm optimization algorithm to find VNs embedding solution. Also, [6] presented embedding algorithm based on discrete particle swarm optimization. They modeled www.ijacsa.thesai.org the VNE problem as binary optimization problem and proved by experiments that their algorithm consumes less computation time.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

“…Energy conservation means to utilize the SN resources in way that saves overall power. This can be done through switching off the underutilizing resources or migrating VN requests [3]- [6]. On the other hand, embedding VN requests over multiple domains had its fair share of attention [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware Virtual Network Embedding Approach for Distributed Cloud

Alzahrani¹,

Shahin²

2017

ijacsa

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“…Recently, due to the importance of the energy issue, energy-aware VN embedding problem also attracts several researchers. Su et al [13,14] and Botero et al [16] proposed two different solutions for energy-efficient VN embedding algorithm. The former focused on the heuristics while the latter focused on the exact algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, in [13,14], they proposed to minimize the energy consumption for the SPs by leveraging metaheuristics, i.e., particle swarm optimization. Nevertheless, the particle swarm optimization (PSO) model has a high opportunity to converge to local optima.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective enhanced particle swarm optimization in virtual network embedding

Zhang

Fang

et al. 2016

J Wireless Com Network

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In network virtualization, one of its core challenges lies in how to map the virtual networks (VNs) to the shared substrate network (SN) that is managed by an infrastructure provider, termed as the virtual network embedding problem. Prior studies on this problem only consider one objective, e.g., maximizing the revenues by mapping more VNs or minimizing the energy cost. In this paper, we addressed the virtual network embedding problem with these two objectives. We leverage niche particle swarm optimization technique to design a meta-heuristic algorithm to solve this problem. Extensive simulations illustrate that the efficiency of our proposed algorithm is better than the state-of-the-art algorithms in terms of both revenue and energy cost.

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