Green virtual networks for cloud computing

Chang, Ruay-Shiung; Wu, Chia‐Ming

doi:10.4108/chinacom.2010.131

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…To manage the energy consumption "green virtual network" must built in cloud computing. In cloud computing an abstraction is provided by network virtualization for network simplifying resource provisions [3].…”

Section: What Is Green Cloud Computing?mentioning

confidence: 99%

A Survey on Green Cloud Computing

Panwar¹,

Rathi²

2015

IJCA

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Network based cloud computing is an alternative to officebased computing. As cloud computing is expanding day by day, energy consumption within cloud is also growing. So there is a need for the management of energy consumption in technology (information and communication) section. Data centers have received more attention in case of energy consumption. This paper contains study on different scheduling approaches on cloud computing.

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Section: What Is Green Cloud Computing?mentioning

confidence: 99%

A Survey on Green Cloud Computing

Panwar¹,

Rathi²

2015

IJCA

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“…Chang et al [9] proposed virtual network architecture with virtual network components such as routers and switches. The proposed architecture provides communication functions for virtual resources in Cloud data centers.…”

Section: Related Workmentioning

confidence: 99%

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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“…When the data center application based on a generic virtual machine, allowing the application workload combined in a smaller number of virtual machines, which can help more efficient use of computer resources. If workload size could allocate in different resources depending on time and space, it could improve energy efficiency and avoid wasting resource [25,26,27,29].…”

Section: Introductionmentioning

confidence: 99%

Green Power Management with Dynamic Resource Allocation for Cloud Virtual Machines

Yang

Wang

Cheng

et al. 2011

2011 IEEE International Conference on High Performance Computing and Communications

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With the development of electronics in governments and business, the implementation of these services are increasing demand for servers. Continued expansion of servers represents our need for more space, power, air conditioning, network, human resources and other infrastructure. Regardless of how powerful servers now become, we do not make good use of all resources and strive for the waste. In this paper, the Green Power Management (GPM) is proposed for load balancing for virtual machine management on cloud. It includes three main phrases: (1) supporting green power mechanism, (2) implementing virtual machine resource monitor onto OpenNebula with web-based interface, and (3) integrating a Dynamic Resource Allocation (DRA) and OpenNebula functions as bases instead of traditionally booting physical machines with command mode.

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Green virtual networks for cloud computing

Cited by 10 publications

References 15 publications

A Survey on Green Cloud Computing

A Survey on Green Cloud Computing

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

Green Power Management with Dynamic Resource Allocation for Cloud Virtual Machines

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