The Energy Demand of Data Centers

Schomaker, Gunnar; Janacek, Stefan; Schlitt, Daniel

doi:10.1007/978-3-319-09228-7_6

Cited by 10 publications

(2 citation statements)

References 12 publications

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“…In this paper, we have explored how EDM can be used to support Green ICT: how to reduce the environmental impacts of ICT hardware and software. The improvements in power effectiveness of data center have mainly focused on new data center designs and more efficient components [87]. However, theoretically, power usage effectiveness is approaching, future efficiency gains will be minor and new approaches are needed.…”

Section: Sustainability Findingsmentioning

confidence: 99%

The Effect of Green Software: A Study of Impact Factors on the Correctness of Software

et al. 2018

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Unfortunately, sustainability is an issue very poorly used when developing software and hardware systems. Lately, and in order to contribute to the earth sustainability, a new concept emerged named Green software which is computer software that can be developed and used efficiently and effectively with minimal or no impact to the environment. Currently, new teaching methods based on students’ learning process are being developed in the European Higher Education Area. Most of them are oriented to promote students’ interest in the course’s contents and offer personalized feedback. Online judging is a promising method for encouraging students’ participation in the e-learning process, although it still has to be researched and developed to be widely used and in a more efficient way. The great amount of data available in an online judging tool provides the possibility of exploring some of the most indicative attributes (e.g., running time, memory) for learning programming concepts, techniques and languages. So far, the most applied methods for automatically gathering information from the judging systems are based on statistical methods and, although providing reasonable correlations, these methods have not been proven to provide enough information for predicting grades when dealing with a huge amount of data. Therefore, the great novelty of this paper is to develop a data mining approach to predict program correctness as well as the grades of the students’ practices. For this purpose, powerful data mining technologies taken from the artificial intelligence domain have been used. In particular, in this study, we have used logistic regression, decision trees, artificial neural network and support vector machines; which have been properly identified as the most suitable ones for predicting activities in the e-learning domains. The results have achieved an accuracy of around 74%, both in the prediction of the program correctness as well as in the practice grades’ prediction. Another relevant issue provided in this paper is a comparison among these four techniques to obtain the best accuracy in predicting grades based on the availability of data as well as their taxonomy. The Decision Trees classifier has obtained the best confusion matrix, and time and memory efficiency were identified as the most important predictor variables. In view of these results, we can conclude that the development of green software leads programmers to implement correct software.

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Section: Sustainability Findingsmentioning

confidence: 99%

The Effect of Green Software: A Study of Impact Factors on the Correctness of Software

et al. 2018

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“…In data centers, huge energy consumption is caused not only by the physical servers and other hardware, but also by the required power supply and cooling system. As such, acquiring the most efficient components and coming up with good architectural designs and configurations regarding energy demand, availability, and performance are deemed crucial [6]. In [7], they presented a cloud infrastructure that combines on-demand allocation of resources with opportunistic provisioning of cycles from idle cloud nodes which aims to improve the utilization of Infrastructure Clouds.…”

Section: Performance and Energy-efficiency Tradeoff In Cloud Data Cenmentioning

confidence: 99%

Goal-driven Optimization Strategy for Energy and Performance-Aware Data Centers for Cloud-Based Wind Farm CMS

2016

KSII TIIS

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A cloud computing system can be characterized by the provision of resources in the form of services to third parties on a leased, usage-based basis, as well as the private infrastructures maintained and utilized by individual organizations. To attain the desired reliability and energy efficiency in a cloud data center, trade-offs need to be carried out between system performance and power consumption. Resolving these conflicting goals is often the major challenge encountered in the design of optimization strategies for cloud data centers. The work presented in this paper is directed towards the development of an Energy-efficient and Performance-aware Cloud System equipped with strategies for dynamic switching of optimization approach. Moreover, a platform is also provided for the deployment of a Wind Farm CMS (Condition Monitoring System) which allows ubiquitous access. Due to the geographically-dispersed nature of wind farms, the CMS can take advantage of the cloud's highly scalable architecture in order to keep a reliable and efficient operation capable of handling multiple simultaneous users and huge amount of monitoring data. Using the proposed cloud architecture, a Wind Farm CMS is deployed in a virtual platform to monitor and evaluate the aging conditions of the turbine's major components in concurrent, yet isolated working environments.

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The Carbon Footprint of Streaming Media: Problems, Calculations, Solutions

Marks

Przedpełski

2022

Palgrave Studies in Media and Environmental Communication

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The Energy Demand of Data Centers

Cited by 10 publications

References 12 publications

The Effect of Green Software: A Study of Impact Factors on the Correctness of Software

The Effect of Green Software: A Study of Impact Factors on the Correctness of Software

Goal-driven Optimization Strategy for Energy and Performance-Aware Data Centers for Cloud-Based Wind Farm CMS

The Carbon Footprint of Streaming Media: Problems, Calculations, Solutions

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