Green data center with IoT sensing and cloud-assisted smart temperature control system

Liu, Qiang; Ma, Yujun; Alhussein, Musaed; Zhang, Yin; Peng, Limei

doi:10.1016/j.comnet.2015.11.024

Cited by 100 publications

(42 citation statements)

References 24 publications

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“…Based on existing literature [150][151][152][153][154][155], cooling management consists of the following components: i) cooling management techniques and ii) cooling plant as shown in Figure 25. Each of these taxonomy elements are discussed below along with relevant examples.…”

Section: Cooling Management Based Taxonomymentioning

confidence: 99%

“…There are two types of temperature classification for three different locations with different range of temperature: 1) supply temperature and 2) return temperature. The different types of cooling plants are: 1) Direct Expansion (DE) air cooled systems, 2) DE glycol cooled systems and 3) chilled water systems [152] [154]. DE air cooled system contains CRAC and an air-cooled condenser as a HRS.…”

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confidence: 99%

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A Taxonomy and Future Directions for Sustainable Cloud Computing

2018

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The cloud computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT) based applications the usage of cloud services is increasing exponentially. The next generation of cloud computing must be energy-efficient and sustainable to fulfil the end-user requirements which are changing dynamically. Presently, cloud providers are facing challenges to ensure the energy efficiency and sustainability of their services. The usage of large number of cloud datacenters increases cost as well as carbon footprints, which further effects the sustainability of cloud services. In this paper, we propose a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups. Further, the current research on sustainable cloud computing is organized into several categories: application design, sustainability metrics, capacity planning, energy management, virtualization, thermal-aware scheduling, cooling management, renewable energy and waste heat utilization. The existing techniques have been compared and categorized based on the common characteristics and properties. A conceptual model for sustainable cloud computing has been proposed along with discussion on future research directions.

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Section: Cooling Management Based Taxonomymentioning

confidence: 99%

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confidence: 99%

A Taxonomy and Future Directions for Sustainable Cloud Computing

2018

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“…Nonetheless, the network infrastructures for supporting huge network capacity are estimated to account for 12% of the total power consumption at present, and this portion will increase to 20% by 2020 . Hence, energy efficiency in optical core network needs to be improved correspondingly for better supporting 5G ultradense cellular network …”

Section: Introductionmentioning

confidence: 99%

“…21,22 Hence, energy efficiency in optical core network needs to be improved correspondingly for better supporting 5G ultradense cellular network. [23][24][25][26] Besides the ever-increasing network traffic volume and power consumption, network traffic also shows obvious heterogeneity in bandwidth requirements among different applications. [27][28][29] For example, some traffic demands may require transmission bandwidth that is less than 10 Gbps, [30][31][32] but some may require huge transmission bandwidth larger reach up to 20, 40, or even 100 Gbps.…”

Section: Introductionmentioning

confidence: 99%

Energy‐efficient multiperiod planning of optical core network to support 5G networks

Peng

Hao

et al. 2017

Trans. Emerging Tel. Tech.

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With the rapid growth of traffic and power consumption in information and communication technology industry for decades, improving energy efficiency has become a challenging issue in recent years. Especially, the pervasive access to Internet via mobile devices will cause wireless traffic volume increment by 1000+ times in near future, which is expected to be carried by the fifth generation ultradense cellular network. Following this trend, the core network where optical fibers are deployed to deliver aggregated traffic needs upgrading to accommodate the increasing traffic volume. To match with the traffic growth rate, we should conduct efficient network planning over multiple time periods. Moreover, considering the heterogeneity in the bandwidth requirements among various network services, flexible mixed line rate scheme is adopted in network provisioning. In this paper, we study the long‐term multiperiod planning of optical core network by considering the traffic growth with the objective of high energy‐efficiency. Three planning strategies, say All‐period, Incremental, and End‐of‐Life, respectively, are studied and formulated as mixed integer linear programming models with the objective to minimize power consumption of optical core network. Given the estimation on the reduction of power consumed by various network devices, we can deploy network devices flexibly in each period through the optimization models. Performance of the proposed models is also evaluated and compared via case studies.

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“…The Internet of Things technology is a network expanded to Internet-enabled objects, whose main function is to connect these objects [1]. It not only greatly improves the convenience of networks but also meets needs that people could not imagine before [2,3].…”

Section: Introductionmentioning

confidence: 99%

The Fusion Model of Multidomain Context Information for the Internet of Things

Jia

Liu

Guan

et al. 2017

Wireless Communications and Mobile Computing

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The Internet of Things aims to provide the user with deep adaptive intelligence services according to the user's personalized characteristics. Most of the characteristics are presented in the form of high-level context. But it often lacks methods to obtain high-level context information directly in the Internet of Things. In this paper, so as to achieve the corresponding high-level context information using the specific low-level multidomain context directly obtained by different sensors in the Internet of Things, we present a machine learning method to construct a context fusion model based on the feature selection algorithm and the multiclassification algorithm. First, we propose a wrapper feature selection method based on the genetic algorithm to obtain a simpler and more important subset of the context features from the low-level multidomain context, by defining a suitable fitness function and a convergence condition. Then, we use the decision tree algorithm which is a multiclassification algorithm, based on the rules obtained by training the subset of context features, to determine which high-level context the record set of the low-level context information belongs to. Experiments confirm that the model can be used to achieve higher classification accuracy without more significant time consumption.

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Green data center with IoT sensing and cloud-assisted smart temperature control system

Cited by 100 publications

References 24 publications

A Taxonomy and Future Directions for Sustainable Cloud Computing

A Taxonomy and Future Directions for Sustainable Cloud Computing

Energy‐efficient multiperiod planning of optical core network to support 5G networks

The Fusion Model of Multidomain Context Information for the Internet of Things

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