Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization

Nam, Tran Manh; Thanh, Nguyen Huu; Hieu, Hoang Trung; Manh, Nguyen Tien; Huynh, Nguyễn Văn; Tuan, Hoang Duong

doi:10.1016/j.comnet.2017.06.007

Cited by 19 publications

(9 citation statements)

References 17 publications

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“…Another work [33] joined Sliceable-Transponders reusability together with DC consolidation, for an Energy-Aware Virtual Optical Network Embedding (EA-VONE). Still as an attempt to join network virtualization to server's consolidation, the work [28] tried to keep DC's network maintaining a Minimum Spanning Tree (MST) for minimum connectivity between servers. In doing so, VMs should cross fewer short hops, thereby reducing the power consumption of network devices.…”

Section: Related Workmentioning

confidence: 99%

“…The arising challenge consists on effectively managing REs availabilities with a view to reduce intra and inter cloud resources' carbon emissions. Recent studies have only investigated intra-cloud resources' carbon emissions reduction [28] [30] [31], without focusing on multi-domain inter-cloud networking resources' carbon emissions. This further incites the need to adopt carbon emission-based intra and inter cloud resources' ranking approach, aiming toward green substrate devices selection.…”

Section: Introductionmentioning

confidence: 99%

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Parallel, Proactive and Power Efficient Virtual Network Embedding in a Green and Distributed SD-ODCN Architecture

et al. 2021

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With a view to overcome internet ossification problem, various Virtual Network Embedding (VNE) approaches has been proposed in last few years. Nevertheless, most of prior approaches neglect some major operational requirements implied by the inherent virtualization platforms. In the case of SD-ODCN architecture, a crucial operational requirement instance is the ability to route application-specific flows or wavelengths dynamically and efficiently across multi-tenant network providers. With the perpetual bursting of clouds and IP traffics, efficient dynamic VNE not only consists on maximizing ISPs and cloud Service Provider (SP) revenues, but also involves a strong need to reduce carbon emissions. In this paper, we introduce a new Parallel, Proactive and Power Efficient VNE in a Green and Distributed SD-ODCN architecture. We first formulate a Mixed Integer Linear Programming (MILP) model purposing to maximize total intra Data Center (DC)' servers and inter networking resources power efficiency as a function of users' request rates. Afterward, we proposed a new green location-aware, Parallel Global resource Topology Ranking (PGTR) method, prioritizing first the greenest server and network nodes. Depending on resulted ranking process classes, a Parallel and Proactive VNE (PPVNE) is therefore proposed to effectively maximize total DC's and networking resources power efficiency. After implementing the whole proposed algorithms namely (PGTR-PPVNE) under NSFNET network topology related data, extensive simulations results proved the improvement of the proposed (PGTR-PPVNE) approach over four other benchmark methods. More precisely, the proposed (PGTR-PPVNE) achieved 6.87% decrease, 10.77% increase, 58.54 % decrease and a 1.15% increase over the proactive ACO-VNE benchmark approach, respectively in terms of total power consumption, total power efficiency, requests response times and acceptance ratio.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parallel, Proactive and Power Efficient Virtual Network Embedding in a Green and Distributed SD-ODCN Architecture

et al. 2021

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“…However, these selection types often lead to resource wastage and fragmentation. So to deal with this issue, this author [48] presents a combination of network embedding and server consolidation considering the total data center virtualization DCV. This approach considers fat tree topology for its minimum oversubscription ratio and keeps the DC network maintains a minimum spanning tree (MST) for minimum connectivity between servers.…”

Section: Virtual Network Embedding (Vne)mentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

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Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

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“…Equation (20) calculates a new velocity on the dth dimension for each particle (potential solution). Equation (21) updates each particle's position in solution space.…”

Section: ) Trainingmentioning

confidence: 99%

“…We compare VCMM with a prior approach Threshold based method (TbM) [20]. TbM is a related method that focuses on VM consolidation in datacenters.…”

Section: ) the Performance And Efficiency Comparison Of The Vcmm Witmentioning

confidence: 99%

Virtualized Network Function Consolidation Based on Multiple Status Characteristics

Su-bin

Wang

2019

IEEE Access

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In network function virtualization (NFV), network function implemented in software on the generally shared servers is called virtualized network function (VNF). The characteristics of virtualization provide service function chain (SFC) provisioning with the required flexibility by allowing for placing VNFs anywhere and anytime. However, there may be a large number of servers with low utilization across the network at the low-load time, leading to enormous loss of energy. VNF consolidation, as an effective way to address this issue, allows for energy saving by consolidating VNFs into as fewer servers as possible. Unfortunately, it involves multiple conflicting goals, such as energy saving, bandwidth usage minimization, and migration cost reduction, which makes it hard to implement in practical environments. In order to achieve the tradeoff among these goals, this paper proposes a VNF consolidation method, VCMM, which determines appropriate servers to be turned off by leveraging neural network with multiple network status characteristics as input. The neural network is trained using the particle swarm optimization algorithm (PSO). VCMM migrates VNFs on servers to be turned off by adopting a greedy mechanism. Through extensive simulations, we demonstrate that the proposed VCMM outperforms prior approaches in terms of energy, bandwidth, and migration costs. INDEX TERMS Multiple status characteristics, network function virtualization (NFV), virtualized network function consolidation.

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Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization

Cited by 19 publications

References 17 publications

Parallel, Proactive and Power Efficient Virtual Network Embedding in a Green and Distributed SD-ODCN Architecture

Parallel, Proactive and Power Efficient Virtual Network Embedding in a Green and Distributed SD-ODCN Architecture

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

Virtualized Network Function Consolidation Based on Multiple Status Characteristics

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