Green Cloud Framework for Improving Carbon Efficiency of Clouds

Garg, Saurabh; Yeo, Chee Shin; Buyya, Rajkumar

doi:10.1007/978-3-642-23400-2_45

Cited by 69 publications

(49 citation statements)

References 12 publications

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“…Both research studies point out the significance of the geographic location of the cloud infrastructure towards the increasing of the eco-efficiency of a service deployment. In addition, in [25], a framework for optimizing the carbon efficiency in clouds is presented, which is based on the installation of a registry with offers from cloud providers including data about their CO 2 emission rate, average DCiE (Data Center infrastructure Efficiency), VM power efficiency, prices, etc. ; all this information has to be updated by the provider though.…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

Estimation and forecasting of ecological efficiency of virtual machines

Katsaros

Stichler

Subirats

et al. 2016

Future Generation Computer Systems

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The massive development of the cloud marketplace is leading to an increase in the number of the Data Centers (DCs) globally and eventually to an increase of the CO 2 related footprint. The calculation of the impact of Virtual Machines (VMs) on the environment is a challenging task, not only due to the technical difficulties but also due to the lack of information from the energy providers. The ecological efficiency of a system captures the relationship between the performance of the system with its environmental footprint. In this paper we present a methodology for the estimation and prediction of the ecological efficiency of VMs in private cloud infrastructures. We specifically focus on the information management starting from the energy resources in a region, the energy consumption and the performance of the resources and finally the calculation of ecological efficiency of a VM. To this end, we have designed and implemented a framework through which the ecological efficiency of a running VM can be assessed and the ecological efficiency of a VM to be deployed can be forecasted. The presented framework is being evaluated through several private cloud scenarios with VM deployments in hosts located in Germany.

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Section: State Of the Art And Related Workmentioning

confidence: 99%

Estimation and forecasting of ecological efficiency of virtual machines

Katsaros

Stichler

Subirats

et al. 2016

Future Generation Computer Systems

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“…In this work, three policies were used for the choice of the Datacenter: Round Robin (RR), Network Capacity Based (NCB) and CEB -CO 2 Emission Based; where the latter is based on the work by Garg et al [15,16]. The first two policies already exist in the GreenMACC and were used in this work for demonstrating a case-study whereby a new policy is inserted into the GreenMACC and also for evaluating how the LRAM module adapts itself to these changes.…”

Section: Choice Of Datacenter Policiesmentioning

confidence: 99%

“…This automated selection may also be made according to the load imposed on the services GreenMACC. Besides the MACC and the GreenMACC the CAGCA (Carbon Aware Green Cloud Architecture) [16] also has the ability to manage multiple datacenters. The CAGCA presents an architecture that allows green green staggering targeting both energy consumption and the emission of carbon dioxide in addition to worrying about QoS.…”

Section: Comparison Between Greenmacc With Other Architecturesmentioning

confidence: 99%

“…There are works that analyze proposed possuim as many points as processor consumption, consumption of air conditioning, the cost of energy and the emission of CO 2 . The main idea of this analysis PADEVE is send to meta-scheduler a coefficient of carbon emissions of data centers (provided by American Environmental Agency) and through these data make the decision of scheduling [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Green Cloud Meta-Scheduling

et al. 2015

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This article aims to evaluate the flexibility of GreenMACC (Metascheduling Green Architecture to Provide Quality of Service in Cloud Computing). The GreenMACC has a module called LRAM (Local Resource Allocation Manager) to automate the execution of all scheduling policies implemented in the architecture. This module enables the Meta-scheduler automatically adjust for each type of service requested by the user of a private cloud. Due to this function, can be ensure the most appropriate behavior to the principles of GreenIT while worrying about the quality of service. In this paper is shown the importance of the LRAM on GreenMACC. This article is also shown how to include a new policy in GreenMACC in a way that identifies the LRAM and automatically use. Through the performance evaluation of the new policy included it could be concluded that the GreenMACC is a flexible, reliable architecture and the LRAM module enables the automation of choosing the best scheduling mechanism in a private cloud.

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“…This algorithm uses the idea of geographic diversity of data center sites and unpredictability of renewable energies to find the optimal percentage of wind/solar energies in order to reduce the brown energy consumption. Garg et al [11] also consider reducing carbon footprint of Cloud data center sites. They proposed a novel carbon-aware green Cloud architecture, which uses two directories for Cloud providers to register their offered services.…”

Section: Related Workmentioning

confidence: 99%

Energy and Carbon-Efficient Placement of Virtual Machines in Distributed Cloud Data Centers

Khosravi

Garg

Buyya

2013

Euro-Par 2013 Parallel Processing

Self Cite

100

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Abstract. Due to the increasing use of Cloud computing services and the amount of energy used by data centers, there is a growing interest in reducing energy consumption and carbon footprint of data centers. Cloud data centers use virtualization technology to host multiple virtual machines (VMs) on a single physical server. By applying efficient VM placement algorithms, Cloud providers are able to enhance energy efficiency and reduce carbon footprint. Previous works have focused on reducing the energy used within a single or multiple data centers without considering their energy sources and Power Usage Effectiveness (PUE). In contrast, this paper proposes a novel VM placement algorithm to increase the environmental sustainability by taking into account distributed data centers with different carbon footprint rates and PUEs. Simulation results show that the proposed algorithm reduces the CO2 emission and power consumption, while it maintains the same level of quality of service compared to other competitive algorithms.

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Green Cloud Framework for Improving Carbon Efficiency of Clouds

Cited by 69 publications

References 12 publications

Estimation and forecasting of ecological efficiency of virtual machines

Estimation and forecasting of ecological efficiency of virtual machines

Green Cloud Meta-Scheduling

Energy and Carbon-Efficient Placement of Virtual Machines in Distributed Cloud Data Centers

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