Implementation of a Cloud Energy Saving System with Virtual Machine Dynamic Resource Allocation Method Based on OpenStack

Chen, Chien-Chih; Sun, Pei-Lun; Yang, Chao-Tung; Liu, Jung-Chun; Hsu, Chih‐Yu; Wan, Zong-Yue

doi:10.1109/paap.2015.44

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…Dynamic Resource Allocation (DRA) method and Energy Saving method have been introduced in [20]. Their main research problem is to find ways to save and use the idled resources on low loading Virtual Machines (VMs) effectively and to save energy consumption on servers.…”

Section: Related Workmentioning

confidence: 99%

“…From the research problem, researchers in the paper introduced two algorithms to improve traditional DRA and energy saving methods. The proposed DRA algorithm, manage to reduce idled resource on the VMs while the improved energy saving algorithm manage to reduce the energy consumption of the entire cloud cluster [20]. The researchers claims to achieve goal by using the DRA method to reduce the idled resources on the VM and using energy saving method to save the energy consumption of servers.…”

Section: Related Workmentioning

confidence: 99%

“…The energy saving method worked mainly through shutting idle physical machines to save energy consumption with the technique call "live migration". The "Live migration" refers to the process of moving a running VM or application between different physical machines without disconnecting the client or application [20]. Memory, storage, and network connectivity of the VM are transferred from the original guest machine to the destination.…”

Section: Related Workmentioning

confidence: 99%

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The Optimization of Resources Allocation to Minimize Energy Consumption Within Data Center in Cloud

Ayob¹,

Upm²

2019

IJATCSE

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ABSTRACTCloud computing is a ubiquitous technology in computer science which provides some advantages to current users of modern computing. The advantages of these services are extremely significant, but the data centers that offer this kind of service are suffering from over power consumption that impose serious threats to the environment due to the emission of carbon dioxide to the atmosphere. Because of some disadvantages in Dynamic Voltage Frequency Scheduling (DVFS), this paper proposes a cloud scheduling algorithm adapted from Dynamic Resource Allocation Method (DRA) to be developed with the goal of dynamically virtual machines (VMs) migration when resource utilization reaches above or below threshold readings in order to optimize the power consumption towards energy saving in cloud data center. The advantage of having VMs migration mechanism is to have the capability to consolidate VMs in order to optimize the capacity of resources utilization. Thus, at the same time it has the capability to switch off some under-loaded hosts which are below the threshold reading. So in this research, our proposed algorithm is going to optimize the total running machine by switching off the hosts with below 30% of CPU power consumption and migrate all the VMs run under it to other hosts. But, for over-utilized hosts with more than 90% of computing resources consumption, the migration of some VMs run under it will be carried out. When comparing with DVFS in a set of experiment, the proposed algorithm is better in terms of lower power consumption.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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The Optimization of Resources Allocation to Minimize Energy Consumption Within Data Center in Cloud

Ayob¹,

Upm²

2019

IJATCSE

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“…Researchers in [20] defined two algorithms which are Dynamic Resources Allocation (DRA) method and Energy Saving method. By the DRA algorithm, the waste of the idle resources on the VMs can be diminished.…”

Section: Review Of Research Efforts In Cloud Computing Data Center Enmentioning

confidence: 99%

An Adaptive Autonomic Framework for Optimizing Energy Consumption in the Cloud Data Centers

Diouani¹,

Medromi²

2019

IJIES

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Given the development of the Cloud Computing recently, clients and customers using the Cloud for both individual and business needs have expanded to an uncommon scale. This has normally prompted the expanded deployments of Cloud data centers over the globe. As a result, Cloud data centers are seen to be monstrous energy consumers and natural polluters. They require an extraordinary measure of regular energy which has made an effect on the energy supply and natural conditions of the environment. This is the reason why the vulnerability of persistent energy supply, later on, is being referred to. In this way, there is a need of an energy-aware cloud-based system which automatically and efficiently manages and optimize cloud computing data center resources by considering energy consumption as an essential Quality of Service (QoS) parameter. This paper, focus on the energy utilization of the data centers and how this can be limited so as to make the cloud computing greener. Thus, a new autonomic resource optimization manager has been proposed to avail the most optimum level of resources with reduced server energy consumption. The proposed framework has been verified theoretically and tested experimentally. The experimental analysis has demonstrated that the effectiveness of the proposed solution is greater than the state-ofthe-art methods in terms of the achieved results related to reducing energy consumption and response time in cloud computing data centers.

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“…O trabalho de [Yang et al 2013] apresenta uma abordagem que gerencia os recursos, permitindo o desligamento, mas que não exercita a possibilidade de religar nós caso necessário. De forma similar, em [Chen et al 2015] há alocação dinâmica de recursos para economia de energia, mas hosts não são reativados caso haja necessidade.…”

Section: Trabalhos Correlatos E Motivaçãounclassified

Um Protótipo para Experimentos de Eficiência Energética em Nuvem Openstack

Santos¹,

Freitas²

2019

Anais Estendidos Do XXXVII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC Estendido 2019)

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Nuvem computacionais baseadas em infra-estrutura como serviço permitem que recursos computacionais sejam alocados sob demanda de forma flexível para atender a necessidades específicas. Desta forma, um datacenter, hospedando diversos nós para prover a nuvem, pode ter ociosidade de recursos com nós subutilizados consumindo energia elétrica desnecessariamente. Este trabalho propõe a implementação de uma ferramenta que auxilia o gerenciamento energético de hosts de uma nuvem implementada na plataforma Openstack -- uma das mais utilizadas para nuvens IaaS--, permitindo desligar hosts subutilizados, ou ativá-los em caso de aumento de demanda. Esta solução pode ser utilizada em experimentos de Eficiência Energética, permitindo a análise por medição de diferentes políticas e métodos.

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Implementation of a Cloud Energy Saving System with Virtual Machine Dynamic Resource Allocation Method Based on OpenStack

Cited by 7 publications

References 12 publications

The Optimization of Resources Allocation to Minimize Energy Consumption Within Data Center in Cloud

The Optimization of Resources Allocation to Minimize Energy Consumption Within Data Center in Cloud

An Adaptive Autonomic Framework for Optimizing Energy Consumption in the Cloud Data Centers

Um Protótipo para Experimentos de Eficiência Energética em Nuvem Openstack

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