An Environmental Chargeback for Data Center and Cloud Computing Consumers

Curry, Edward; Hasan, Souleiman; White, Mark; Melvin, Hugh

doi:10.1007/978-3-642-33645-4_11

Cited by 14 publications

(7 citation statements)

References 5 publications

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“…The LEI platform has been applied in a number of energy management scenarios including cloud computing [23] and enterprise energy management [24]. In this section we show how LEI can be applied to the management of building energy.…”

Section: Architecturementioning

confidence: 99%

Linking building data in the cloud: Integrating cross-domain building data using linked data

Curry

O’Donnell

Corry

et al. 2013

Advanced Engineering Informatics

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179

127

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a b s t r a c tWithin the operational phase buildings are now producing more data than ever before, from energy usage, utility information, occupancy patterns, weather data, etc. In order to manage a building holistically it is important to use knowledge from across these information sources. However, many barriers exist to their interoperability and there is little interaction between these islands of information.As part of moving building data to the cloud there is a critical need to reflect on the design of cloudbased data services and how they are designed from an interoperability perspective. If new cloud data services are designed in the same manner as traditional building management systems they will suffer from the data interoperability problems.Linked data technology leverages the existing open protocols and W3C standards of the Web architecture for sharing structured data on the web. In this paper we propose the use of linked data as an enabling technology for cloud-based building data services. The objective of linking building data in the cloud is to create an integrated well-connected graph of relevant information for managing a building. This paper describes the fundamentals of the approach and demonstrates the concept within a Small Medium sized Enterprise (SME) with an owner-occupied office building.

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Section: Architecturementioning

confidence: 99%

Linking building data in the cloud: Integrating cross-domain building data using linked data

Curry

O’Donnell

Corry

et al. 2013

Advanced Engineering Informatics

Self Cite

179

127

View full text Add to dashboard Cite

show abstract

“…For example, Google publishes the carbon emissions per query (0.2 g CO 2 ) and per watched minute on YouTube (0.1 g of CO 2 ) [21]. Similarly, in [22] a charge-back model is presented, where the environmental impact of providing data center services to the service consumers is traced back to the consumer. The consumer receives information about the CO 2 intensity of each transaction as well as the overall CO 2 emissions produced by his transactions.…”

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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“…As a further reinforcing factor in favour of green instances, users could be presented with the approximated energy savings arising from choosing green over normal instances. Curry et al [36] denote this type of information environmental chargebacks (EC). Using carbon emission factor (CEF) and power usage efficiency (PUE) defined in [37] we could express it as: EC = CEF * P U E * (VM energy consumption) (2) VMeter developed in [38] might be used to determine the energy consumption of a user's VMs or the approach from [39] for parallel applications.…”

Section: The Green Instance Modelmentioning

confidence: 99%

Take a Break: Cloud Scheduling Optimized for Real-Time Electricity Pricing

Lučanin

Brandić

2013

2013 International Conference on Cloud and Green Computing

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Abstract-Cloud computing revolutionised the industry with its elastic, on-demand approach to computational resources, but has lead to a tremendous impact on the environment. Data centers constitute 1.1-1.5% of total electricity usage in the world. Taking a more informed view of the electrical grid by analysing real-time electricity prices, we set the foundations of a grid-conscious cloud. We propose a scheduling algorithm that predicts electricity price peaks and throttles energy consumption by pausing virtual machines. We evaluate the approach on the OpenStack cloud manager through an empirical approach and show reductions in energy consumption and costs. Finally, we define green instances in which cloud providers can offer such services to their customers under better pricing options.

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An Environmental Chargeback for Data Center and Cloud Computing Consumers

Cited by 14 publications

References 5 publications

Linking building data in the cloud: Integrating cross-domain building data using linked data

Linking building data in the cloud: Integrating cross-domain building data using linked data

Estimation and forecasting of ecological efficiency of virtual machines

Take a Break: Cloud Scheduling Optimized for Real-Time Electricity Pricing

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