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Journal articles on the topic 'Data Cloud center'

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Published: 27 April 2023

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1

Guo, Le Jiang, Feng Zheng, Ya Hui Hu, Lei Xiao, and Liang Liu. "Analysis and Research of Cloud Computing Data Center." Applied Mechanics and Materials 427-429 (September 2013): 2184–87. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2184.

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Cloud computing data centers can be called cloud computing centers. It has put forward newer and higher demands for data centers with the development of cloud computing technologies. This paper will discuss what are cloud computing data centers, cloud computing data center construction, cloud computing data center architecture, cloud computing data center management and maintenance, and the relationship between cloud computing data centers and clouds.
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2

Karamat Khan, Tehmina, Mohsin Tanveer, and Asadullah Shah. "Energy Efficiency in Virtualized Data Center." International Journal of Engineering & Technology 7, no. 4.15 (October 7, 2018): 315. http://dx.doi.org/10.14419/ijet.v7i4.15.23019.

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Industrial and academic communities have been trying to get more computational power out of their investments. Data centers have recently received huge attention due to its increased business value and achievable scalability on public/private clouds. Infra-structure and applications of modern data center is being virtualized to achieve energy efficient operation on servers. Despite of data center advantages on performance, there is a tradeoff between power and performance especially with cloud data centers. Today, these cloud application-based organizations are facing many energy related challenges. In this paper, through survey it has been analyzed how virtualization and networking related challenges affects energy efficiency of data center with suggested optimization strategies.
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Rajput, Ravindra Kumar Singh, Dinesh Goyal, Anjali Pant, Gajanand Sharma, Varsha Arya, and Marjan Kuchaki Rafsanjani. "Cloud Data Centre Energy Utilization Estimation." International Journal of Cloud Applications and Computing 12, no. 1 (January 1, 2022): 1–16. http://dx.doi.org/10.4018/ijcac.311035.

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Due to the growth of the internet and internet-based software applications, cloud data center demand has increased. Cloud data centers have thousands of servers that are 24×7 working for users; it is the strong witness of enormous energy consumption for the operation of the cloud data center. However, server utilization is not remaining the same all the time, so, from an economic feasibility point of view, energy management is an essential activity for cloud resource management. Some well-known energy management techniques for cloud data centers generally used are dynamic voltage and frequency scaling (DVFS), dynamic power management (DPM), and task scheduling-based techniques. The present work is based on an analytical approach to integrating resource provisioning with sophisticated task scheduling; the authors estimate energy utilization by cloud data centers using iDR cloud simulator. The work is intended to optimize power consumption in the cloud data center.
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Yang, Jing Bo, Shu Huang, and Pan Jiang. "Research on Distributed Heterogeneous Data Storage Algorithm in Cloud Computing Data Center." Applied Mechanics and Materials 624 (August 2014): 553–56. http://dx.doi.org/10.4028/www.scientific.net/amm.624.553.

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With the development of cloud computing, data center is also improved. cloud computing data center contains hundreds, even million of servers or PCs. It has many heterogeneous resources. Data center is a key to promise high scalability and resource usage of cloud computing. In addition, replica is introduced into data center, which is an important method to improve availability and performance. In this paper, the research on distributed storage algorithm based on the cloud computing. This algorithm uses the design of system storage level indicators within classification of massive data storage mechanism to solve the allocation problem of data consistency between the data center; and send communication packets between data centers through the cloud computing. The full storage can achieve complete local storage of each data stream, and solve the original data stream unusually large-scale data storage allocation problem.
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Kanniga Devi R., Murugaboopathi Gurusamy, and Vijayakumar P. "An Efficient Cloud Data Center Allocation to the Source of Requests." Journal of Organizational and End User Computing 32, no. 3 (July 2020): 23–36. http://dx.doi.org/10.4018/joeuc.2020070103.

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A Cloud data center is a network of virtualized resources, namely virtualized servers. They provision on-demand services to the source of requests ranging from virtual machines to virtualized storage and virtualized networks. The cloud data center service requests can come from different sources across the world. It is desirable for enhancing Quality of Service (QoS), which is otherwise known as a service level agreement (SLA), an agreement between cloud service requester and cloud service consumer on QoS, to allocate the cloud data center closest to the source of requests. This article models a Cloud data center network as a graph and proposes an algorithm, modified Breadth First Search where the source of requests assigned to the Cloud data centers based on a cost threshold, which limits the distance between them. Limiting the distance between Cloud data centers and the source of requests leads to faster service provisioning. The proposed algorithm is tested for various graph instances and is compared with modified Voronoi and modified graph-based K-Means algorithms that they assign source of requests to the cloud data centers without limiting the distance between them. The proposed algorithm outperforms two other algorithms in terms of average time taken to allocate the cloud data center to the source of requests, average cost and load distribution.
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Bao, Hao. "Homomorphic computing of encrypted data outsourcing in cloud data center." Frontiers in Computing and Intelligent Systems 2, no. 1 (November 23, 2022): 1–3. http://dx.doi.org/10.54097/fcis.v2i1.2482.

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In the era of data explosion, data contains massive information, such as health data, time and place, hydrological waves, etc. In order to process and calculate these data, local Wang networking devices will send data to the cloud data center for outsourcing processing due to their limited storage and computing capabilities. However, our data contains a large amount of private data, so we need to protect the privacy of our outsourced data before outsourcing, so as to protect our personal privacy. At the same time, cloud data centers have strong advantages in data storage and computing capabilities, so cloud data centers are increasingly used.
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Wang, Yi Nuo. "Research and Design of Enterprise-Class Network Data Center Based on Cloud Computing." Applied Mechanics and Materials 651-653 (September 2014): 1893–95. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1893.

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analyzing management situation of current enterprise-class network, introducing cloud computing technique, presenting centre framework of enterprise-class network data center based on cloud computing, stating resource integration way, which includes cloud infrastructure virtualization, cloud platform, and cloud application for different structure. The data center based on cloud computing can support the operation and development in a better way.
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8

Govindasamy, C., and J. Venkatesh. "Data Center Management-Cloud Migration Techniques." Asian Journal of Research in Business Economics and Management 7, no. 10 (2017): 1. http://dx.doi.org/10.5958/2249-7307.2017.00174.8.

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9

Filiposka, Sonja, and Carlos Juiz. "Community-based complex cloud data center." Physica A: Statistical Mechanics and its Applications 419 (February 2015): 356–72. http://dx.doi.org/10.1016/j.physa.2014.10.017.

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10

Volkova, Elena Viktorovna, Anzhelika Andreevna Kostornaya, and Ruslana Aleksandrovna Amikishieva. "THE DETERMINATION OF CLOUD COVER PARAMETERS USING SATELLITE DATA PROCESSING SYSTEMS." Географический вестник = Geographical bulletin, no. 3(54) (2020): 124–34. http://dx.doi.org/10.17072/2079-7877-2020-3-124-134.

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The paper discusses the results of comparing cloud cover properties determined by using polar orbiting satellite data (AVHRR/NOAA and MSU-MR/Meteor-M No. 2) for the European territory of Russia and Western Siberia. The cloud characteristics were computed by two threshold techniques: Complex Threshold Technique (CTT) (developed at the European Centre of the State Research Center ‗Planeta‘) and Cloud Cover Detection Technique (CCDT) (developed at the Siberian Centre of ‗Planeta‘). Pixel-by-pixel comparison was performed for very close in time satellite observations, and it showed that in spite of technical similarity of the two radiometers and little difference between both techniques used for the classifications, the results were not the same. The quality of the MSU-MR classification is significantly worse than that of the two AVHRR classifications. In fact, the MSU-MR derivation of cloud parameters fails in optically thin cirrus and altocumulus clouds, thus underestimating the cloud top height for multilayered clouds. As a result, the cloud top is found to be lower, warmer and less iced in comparison with both AVHRR estimates, regardless of the region and other conditions; on the contrary, the cloud top of low and middle clouds appears to be colder, higher and more iced according to MSU-MR data. The MSU-MR cloud mask is strongly underestimated at night during the cold period of the year. The CTT and CCDT‘s cloud top height, temperature and water phase retrieved by AVHRR data are quite close for both regions.
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Shobirin, Kheri Arionadi, Nyoman Putra Sastra, and Made Sudarma. "Evaluasi Pengembangan Disaster Recovery Center untuk Data Center Universitas Udayana." Majalah Ilmiah Teknologi Elektro 20, no. 1 (April 9, 2021): 169. http://dx.doi.org/10.24843/mite.2021.v20i01.p20.

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Data Center memiliki peran vital dan strategis dalam mendukung operasional perguruan tinggi. Berdasarkan Peraturan Pemerintah No.17 tahun 2019 pasal 20 ayat 1: Setiap pemilik Data Center wajib memiliki Disaster Recovery Center. Evaluasi Pengembangan Disaster Recovery Center untuk Data Center Universitas Udayana dilakukan dengan mempertimbangkan aspek ancaman alam, ancaman manusia, ancaman lingkungan, spesifikasi Data Center, virtualisasi dan teknologi cloud yang digunakan untuk menjaga ketersediaan layanan Data Center bagi Universitas Udayana dengan biaya pengembangan yang paling efisien. Dengan membandingkan biaya untuk pembangunan dan operasional DRC, ditemukan bahwa biaya implementasi Cloud DRC 3 kali lebih tinggi dibanding dengan DRC Konvensional. Biaya komputasi awan yang tinggi berkontribusi 67% terhadap struktur biaya Cloud DRC.
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Yang, S., and X. Zou. "Temperature Profiles and Lapse Rate Climatology in Altostratus and Nimbostratus Clouds Derived from GPS RO Data." Journal of Climate 26, no. 16 (August 6, 2013): 6000–6014. http://dx.doi.org/10.1175/jcli-d-12-00646.1.

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Abstract Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) radio occultation (RO) refractivity profiles in altostratus and nimbostratus clouds from 2007 to 2010 are first identified based on collocated CloudSat data. Vertical temperature profiles in these clouds are then retrieved from cloudy refractivity profiles. Contributions of cloud liquid water content and ice water content are also included in the retrieval algorithm. The temperature profiles and their lapse rates are compared with those from a standard GPS RO wet retrieval without including cloud effects. On average, the temperatures from cloudy retrieval are about 0.5–1.0 K warmer than the GPS RO wet retrieval, except for the altitudes near the nimbostratus base. The differences of temperature between the two methods are largest in summer and smallest in winter. The lapse rate in altostratus clouds is around 6.5°–7.5°C km−1 and does not vary greatly with height. On the contrary, the lapse rate increases significantly with height in nimbostratus clouds, from about 2.5°–3.5°C km−1 near the cloud base to about 5.0°–6.0°C km−1 at cloud center and 6.5°–7.5°C km−1 below the cloud top. Seasonal variability of lapse rate derived from the cloudy retrieval is larger than that derived from the wet retrieval. The lapse rate within clouds is smaller in summer and larger in winter. The mean lapse rate decreases with temperature in all seasons.
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13

LI, YANGYANG, HONGBO WANG, JIANKANG DONG, JUNBO LI, and SHIDUAN CHENG. "Differentiated Bandwidth Guarantees for Cloud Data Centers." Journal of Interconnection Networks 14, no. 03 (September 2013): 1360002. http://dx.doi.org/10.1142/s0219265913600025.

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By means of virtualization, computing and storage resources are effectively multiplexed by different applications in cloud data centers. However, there lacks useful approaches to share the internal network resource of cloud data centers. Invalid network sharing not only degrade the performance of applications, but also affect the efficiency of data center operation. To guarantee network performance of applications and provide fine-grained service differentiation, in this paper, we propose a differentiated bandwidth guarantee scheme for data center networks. Utility functions are constructed according to the throughput and delay sensitive characteristics of different applications. Aiming to maximize the utility of all applications, the problem is formulated as a multi-objective optimization problem. We solve this problem using a heuristic algorithm: the elitist Non-Dominated Sorted Genetic Algorithm-II(NSGA-II), and we make a multi-attribute decision to refine the solutions. Extensive simulations are conducted to show that our scheme provides minimum band-width guarantees and achieves more fine-grained service differentiation than existing approaches. The simulation also verifies that the proposed mechanism is suitable for arbitrary data center architectures.
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14

Uzaman, Sardar Khaliq, Atta ur Rehman Khan, Junaid Shuja, Tahir Maqsood, Faisal Rehman, and Saad Mustafa. "A Systems Overview of Commercial Data Centers." International Journal of Information Technology and Web Engineering 14, no. 1 (January 2019): 42–65. http://dx.doi.org/10.4018/ijitwe.2019010103.

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Data center facilities play a vital role in present and forthcoming information and communication technologies. Internet giants, such as IBM, Microsoft, Google, Yahoo, and Amazon hold large data centers to provide cloud computing services and web hosting applications. Due to rapid growth in data center size and complexity, it is essential to highlight important design aspects and challenges of data centers. This article presents market segmentation of the leading data center operators and discusses the infrastructural considerations, namely energy consumption, power usage effectiveness, cost structure, and system reliability constraints. Moreover, it presents data center network design, classification of the data center servers, recent developments, and future trends of the data center industry. Furthermore, the emerging paradigm of mobile cloud computing is debated with respect to the research issues. Preliminary results for the energy consumption of task scheduling techniques are also provided.
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15

Ding, Jie, Hai Yun Han, and Ai Hua Zhou. "A Data Placement Strategy for Data-Intensive Cloud Storage." Advanced Materials Research 354-355 (October 2011): 896–900. http://dx.doi.org/10.4028/www.scientific.net/amr.354-355.896.

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Data-Intensive applications in power systems often perform complex computations which always involve large amount of datasets. In a distributed environment, an application may needs several datasets located in different data centers which faces two challenges including the high cost of data movements between data centers and data dependencies within the same data centers. In this paper, a data placement strategy among and within data centers in a cloud environment is proposed. Datasets are placed in different centers by a clustering scheme based on the data dependencies. And within the center, data is partitioned and replicated using consistent hashing. Simulations show that the algorithm can effectively reduce the cost of data movements and perform a evenly data distribution.
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16

R., Dhaya, Ujwal U. J., Tripti Sharma, Mr Prabhdeep Singh, Kanthavel R., Senthamil Selvan, and Daniel Krah. "Energy-Efficient Resource Allocation and Migration in Private Cloud Data Centre." Wireless Communications and Mobile Computing 2022 (February 28, 2022): 1–13. http://dx.doi.org/10.1155/2022/3174716.

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The level of difficulty that can be envisioned in a cloud data center will not grow with convention. As a result, all hosts should have a standard and pervasive collection of memory and communication characteristics in order to lower ownership costs and operate virtual machine instances. This solution includes fundamental foundations and integrated component basics that will allow an IT or federal agency to embrace cloud computing domestically via private virtual cloud data centers. These private cloud data centers would later be developed to purchase and develop IT services on the outside. They are well aware of the obstacles to cloud computing’s acceptance, including concerns about credibility, privacy, interoperability, and marketplaces. In addition, this procedure describes critical standards and collaborations to address these issues. Ultimately, it offers a coherent response to deploying safe data centers using cloud computing services from both a technological and an IT strategic standpoint. To foster creativity, invention, learning, and enterprise, a private data center and cloud computing must be established to combine the activities of different research teams. In the framework of energy-efficient distribution of resources in private cloud data center architecture, we focus on system structure investigations. On the other hand, we want to equip private cloud providers with the current design and performance analysis for energy-efficient resource allocation. The methodology should be adaptable enough to support a wide range of computing systems, as well as on-demand and extensive resource providing approaches, cloud environment scheduling, and bridging the gap between private cloud users and a complete image of offers.
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Pawlish, Michael J., Aparna S. Varde, and Stefan A. Robila. "The Greening of Data Centers with Cloud Technology." International Journal of Cloud Applications and Computing 5, no. 4 (October 2015): 1–23. http://dx.doi.org/10.4018/ijcac.2015100101.

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This article presents a decision support system to provide green or energy efficient solutions for data centers that maintain computers and peripherals to serve organizations. Traditionally, data centers catered to all operations using in-house servers. Cloud technology provides alternatives to outsource operations heading towards greenness. However, using cloud services for all data center operations may have its pitfalls. In this paper, the authors analyze various data center parameters such as carbon footprint and power usage effectiveness along with cloud-based and server-based models. They consider data mining techniques of decision trees and case based reasoning in their work. Among other findings, they head towards a hybrid model that meets the demands of productivity, energy efficiency and related factors. These findings lead to the development of the decision support system. The authors describe the research, development and evaluation of the system. They conclude with important outcomes deployed in real-world scenarios in data center management.
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Naha, Ranesh Kumar, Mohamed Othman, and Nasrin Akhter. "EVALUATION OF CLOUD BROKERING ALGORITHMS IN CLOUD BASED DATA CENTER." Far East Journal of Electronics and Communications 15, no. 2 (September 14, 2015): 85–98. http://dx.doi.org/10.17654/fjecdec2015_085_098.

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T., Deepika, and Prakash P. "Power consumption prediction in cloud data center using machine learning." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 1524. http://dx.doi.org/10.11591/ijece.v10i2.pp1524-1532.

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The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.
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Khani, Hadi, and Hamed Khanmirza. "Randomized routing of virtual machines in IaaS data centers." PeerJ Computer Science 5 (September 2, 2019): e211. http://dx.doi.org/10.7717/peerj-cs.211.

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Cloud computing technology has been a game changer in recent years. Cloud computing providers promise cost-effective and on-demand resource computing for their users. Cloud computing providers are running the workloads of users as virtual machines (VMs) in a large-scale data center consisting a few thousands physical servers. Cloud data centers face highly dynamic workloads varying over time and many short tasks that demand quick resource management decisions. These data centers are large scale and the behavior of workload is unpredictable. The incoming VM must be assigned onto the proper physical machine (PM) in order to keep a balance between power consumption and quality of service. The scale and agility of cloud computing data centers are unprecedented so the previous approaches are fruitless. We suggest an analytical model for cloud computing data centers when the number of PMs in the data center is large. In particular, we focus on the assignment of VM onto PMs regardless of their current load. For exponential VM arrival with general distribution sojourn time, the mean power consumption is calculated. Then, we show the minimum power consumption under quality of service constraint will be achieved with randomize assignment of incoming VMs onto PMs. Extensive simulation supports the validity of our analytical model.
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Singh, Jitendra, and Vikas Kumar. "Implementation of User-End Broker Policy to Improve the Reliability of Cloud Services." International Journal of Cloud Applications and Computing 3, no. 4 (October 2013): 13–27. http://dx.doi.org/10.4018/ijcac.2013100102.

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Outage in cloud computing services is a critical issue and is primarily attributed to the single data center connectivity. To address the cloud outage, this work proposes a model for the subscription and selection of more than one data center. Selection of data center can be determined by the usage of broker at the user ends itself. Provision of broker at user's end reduces the overhead at provider's end; as a result performance of cloud data center improves. For the selection of appropriate data center, broker takes the feedback from the available data centers, and select one of them. During the selection of cloud, their status (up/down) at that particular time is also considered. In case of outage at one data center, other can be selected from the available list. Broker also facilitates the homogeneous use of cloud by allotting the load to less congested data centers. Experimental results revealed that multiple data center approach is not only helpful in countering the outage (as other data center can be selected from the broker) but also the usage cost.
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Shokeen, Vivek, and Lalit Malik. "Cloud Computing – Data Center – Access from Anywhere." International Journal of Computer Science and Mobile Computing 11, no. 6 (June 30, 2022): 212–19. http://dx.doi.org/10.47760/ijcsmc.2022.v11i06.015.

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Now days we are attached with our electronic gadgets either offline or online in such a way as we breathe, as without breathing we can’t alive, same like these electronics have become a vital part of our daily life. As of now by growing demand of these gadgets either in office work, or in research work or in various technical fields. As the use of computers increases as in day in and day out, the relevant resources that we need also go up. For companies like Oracle, IBM and Microsoft, utilizing the resources and establishing a huge infrastructure or network is not a big issue for these big giants. But for the new startup and small enterprises But when it comes to smaller enterprises, expensiveness becomes a huge factor. At the user or developer end we are facing a lot of issues like hardware disruption, various bugs in software, network errors. So this was such a big hassle for the large community of computer science in all over the world. Cloud Computing offers a quick fix to this situation. This technology has been completely transformed the way in which computing is moved up from PCs and as well as for the single user to enterprise via various servers to a ‘cloud’ of computers e.g: web servers, application servers, database servers and so on. A cloud is a virtualized server grid which can provide the different computing resources through IAAS (Infrastructure as a service),PAAS(platform as a service),SAAS(Software as a service) of their clients. The underlying details of how it is implemented which is not direct to end user. The data and the services provided reside in extensively scalable data centers so anyone can use or access it from any part of the globe. Various big giants like Google, Microsoft, Yahoo, IBM and Amazon have started providing cloud computing services from the individual user either paid or free upto some extent or to other small enterprises, big businesses through various models like B2B,B2C,C2C etc. Amazon, salesforce is the trailblazer in the field of cloud computing. Every big and small enterprise, companies which are doing their businesses online using cloud services for the storing information so that later on they will extract some meaning full insights for the growth of businesses. Cloud Computing is finding use in various areas like web hosting, parallel batch processing, graphics rendering, financial modeling, web crawling, genomics analysis, etc.
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Li, Bo, Baochun Li, and Fangming Liu. "Cloud and data center performance [Guest Editorial]." IEEE Network 27, no. 4 (2013): 6–7. http://dx.doi.org/10.1109/mnet.2013.6574658.

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Yang, Jun, Wenjing Xiao, Chun Jiang, M. Shamim Hossain, Ghulam Muhammad, and Syed Umar Amin. "AI-Powered Green Cloud and Data Center." IEEE Access 7 (2019): 4195–203. http://dx.doi.org/10.1109/access.2018.2888976.

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Wang, Liang. "Cloud-Based Next-Generation Data Center Design." Advanced Materials Research 1078 (December 2014): 439–43. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.439.

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With the constant development of computer network technology, more and more enterprises have built their own data center in their networks, through which offers a variety of network applications and services. The increasing business volumes and rich types, as well as the amount and scale of data call for higher requirements in management and maintenance. Thus the traditional data center model can’t meet the demand obviously.
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Xie, Zhixin. "Data Center Based on Cloud Computing Technology." IJIIS: International Journal of Informatics and Information Systems 6, no. 1 (January 25, 2023): 31–37. http://dx.doi.org/10.47738/ijiis.v6i1.128.

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Zhao, Dalin. "Status of Cloud Computing in Data Intensive Data Center." Journal of Physics: Conference Series 1881, no. 3 (April 1, 2021): 032089. http://dx.doi.org/10.1088/1742-6596/1881/3/032089.

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Wi, Yukyeong, and Jin Kwak. "Secure data management scheme in the cloud data center." International Journal of Advanced Media and Communication 5, no. 2/3 (2014): 225. http://dx.doi.org/10.1504/ijamc.2014.060493.

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Khajehei, Kamyab. "Green Cloud and reduction of energy consumption." Computer Engineering and Applications Journal 4, no. 1 (February 18, 2015): 51–60. http://dx.doi.org/10.18495/comengapp.v4i1.119.

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By using global application environments, cloud computing based data centers growing every day and this exponentially grows definitely effect on our environment. Researchers that have a commitment to their environment and others which was concerned about the electricity bills came up with a solution which called “Green Cloud”. Green cloud data centers based on how consume energy are known as high efficient data centers. In green cloud we try to reduce number of active devices and consume less electricity energy. In green data centers toke an advantage of VM and ability of copying, deleting and moving VMs over the data center and reduce energy consumption. This paper focused on which parts of data centers may change and how researchers found the suitable solution for each component of data centers. Also with all these problems why still the cloud data centers are the best technology for IT businesses.
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Yao, Meng Di, Dong Lin Chen, and Xin Chen. "Scheduling System for Cloud Federation across Multi-Data Center." Applied Mechanics and Materials 457-458 (October 2013): 839–43. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.839.

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In the cloud computing federation, the method of cloud computing federation resource scheduling has been introduced to allocate the users requested tasks reasonably to the providers. In the present, single cloud providers method and system of resource scheduling dont apply to the cloud federation environment. Therefore, the solution to the problem of resource scheduling in cloud federation cross data center has become a key technology. The system, mainly about the resource scheduling algorithms across data center, presents the framework and major function of the cloud federation environment cross the datacenter. Furthermore, through Cloudsim, a Web server based system platform was built. Finally, the system proved that it can meet the complicated large-scale demand of users as well as increase the efficiency and profit of resource scheduling among the providers in cloud federation.
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Chi, Yongfang. "Network System Structure Design for Data Centers in Large Enterprises Using Cloud Computing." International Journal of Enterprise Information Systems 14, no. 2 (April 2018): 87–97. http://dx.doi.org/10.4018/ijeis.2018040106.

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In this article, the author discusses the network system structure design for data centers in large enterprises using cloud computing. First, she designs a framework for a cloud data center in large enterprise systems. Second, she establishes the data center network frame based on software-defined networking (SDN) and the algorithm procedure. Third, she studies the moving algorithm of a virtual machine and the broadband allocation mechanism of a data center of a large enterprise network system along with the mathematical model. Finally, the author carries out the performance simulation analysis of the data center based on cloud computing. Finally, she carries out the performance comparison between the new data center at a large enterprise network system and traditional systems. The author shows that the new data center model in large enterprise network systems has a better network capacity and fault tolerance.
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Zhang, Yi, and Yi Min Su. "Research on Resource Scheduling Algorithm in Cloud Computing Data Center." Advanced Materials Research 926-930 (May 2014): 2050–53. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2050.

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In recent years, with the rapid development of Internet and virtualization technology, cloud computing, which providing users with on-demand services, has become a research hotspot. Under the environment of cloud computing, the datacenter, consisted by hardware and software, is a loosely coupled resource sharing architecture. The existing cloud computing's inadequacies are as following three aspects: 1. For lacking of real adequate and effective transaction of global bidirectional-way selection, the revenue of most of cloud resource provider is too low. 2. Since not fully considering the scheduling of multi-dimensional cloud resources, existing cloud computing's utilization for multi-dimensional cloud resource is too low. 3. Because existing cloud datacenter does not fully consider the energy consumption of communication between the cloud tasks, its energy consumption is too high. Resource scheduling is a major research direction of cloud computing. First, we make a in-depth investigation and analysis of the research status of cloud computing resource scheduling, and then focus on resource scheduling method to reduce the energy consumption of cloud computing data center. Finally we set an important future research direction of cloud computing resource management research in order to provide a useful reference for cloud computing research.
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33

Zheng, Bing, Xiaoying Zhang, and Dawei Yun. "Virtual technology of cache and real-time big data distribution in cloud computing big data center." Journal of Intelligent & Fuzzy Systems 39, no. 6 (December 4, 2020): 8917–25. http://dx.doi.org/10.3233/jifs-189289.

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By comparing several cloud computing of big data network center during COVID-19, this paper proposes a new topology model, which realizes two functions of cloud computing big data center caching and big data real-time distribution. In addition, cloud computing network requires higher performance than traditional application big data center, which makes the consideration of network platform construction performance different from the traditional understanding. During COVID-19, we deeply understood the underlying attributes of cloud, combined with the topology model, we can realize the decoupling of cloud computing big data system, change the situation of direct connection between upstream and downstream, and have more reliable and efficient transmission of message and command big data.
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34

Lin, L., X. Zou, R. Anthes, and Y.-H. Kuo. "COSMIC GPS Radio Occultation Temperature Profiles in Clouds." Monthly Weather Review 138, no. 4 (April 1, 2010): 1104–18. http://dx.doi.org/10.1175/2009mwr2986.1.

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Abstract Thermodynamic states in clouds are closely related to physical processes such as phase changes of water and longwave and shortwave radiation. Global Positioning System (GPS) radio occultation (RO) data are not affected by clouds and have high vertical resolution, making them ideally suited to cloud profiling on a global basis. By comparing the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO refractivity data with those of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis and ECMWF analysis for soundings in clouds and clear air separately, a systematic bias of opposite sign was found between large-scale global analyses and the GPS RO observations under cloudy and clear-sky conditions. As a modification to the standard GPS RO wet temperature retrieval that does not distinguish between cloudy- and clear-sky conditions, a new cloudy retrieval algorithm is proposed to incorporate the knowledge that in-cloud specific humidity (which affects the GPS refractivities) should be close to saturation. To implement this new algorithm, a linear regression model for a sounding-dependent relative humidity parameter α is first developed based on a high correlation between relative humidity and ice water content. In the absence of ice water content information, α takes an empirical value of 85%. The in-cloud temperature profile is then retrieved from GPS RO data modeled by a weighted sum of refractivities with and without the assumption of saturation. Compared to the standard wet retrieval, the cloudy temperature retrieval is consistently warmer within clouds by ∼2 K and slightly colder near the cloud top (∼1 K) and cloud base (1.5 K), leading to a more rapid increase of the lapse rate with height in the upper half of the cloud, from a nearly constant moist lapse rate below and at the cloud middle (∼6°C km−1) to a value of 7.7°C km−1, which must be closer to the dry lapse rate than the standard wet retrieval.
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35

Xiao, Lei, Wei Jiang, Fang Xin Chen, Le Jiang Guo, and Ya Hui Hu. "A Survey of Cloud Computing Data Virtualization Service." Applied Mechanics and Materials 441 (December 2013): 1016–19. http://dx.doi.org/10.4028/www.scientific.net/amm.441.1016.

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Cloud computing is becoming a mainstream aspect of information technology. How to efficiently manage the cloud resources across multiple cloud domains is critical for providing continuous cloud services. This paper introduces the principle and review recent research progress on cloud service to support network virtualization. It presents a framework of network-Cloud convergence based on data center network and gives a survey on key technologies for realizing cloud center and service; the reliability of cloud applications can be greatly improved.
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36

Liu, Qiong, Hailin Wang, Xiaoqin Lu, Bingke Zhao, Yonghang Chen, Wenze Jiang, and Haijiang Zhou. "Tropical Cyclone Temperature Profiles and Cloud Macro-/Micro-Physical Properties Based on AIRS Data." Atmosphere 11, no. 11 (November 2, 2020): 1181. http://dx.doi.org/10.3390/atmos11111181.

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We used the observations from Atmospheric Infrared Sounder (AIRS) onboard Aqua over the northwest Pacific Ocean from 2006–2015 to study the relationships between (i) tropical cyclone (TC) temperature structure and intensity and (ii) cloud macro-/micro-physical properties and TC intensity. TC intensity had a positive correlation with warm-core strength (correlation coefficient of 0.8556). The warm-core strength increased gradually from 1 K for tropical depression (TD) to >15 K for super typhoon (Super TY). The vertical areas affected by the warm core expanded as TC intensity increased. The positive correlation between TC intensity and warm-core height was slightly weaker. The warm-core heights for TD, tropical storm (TS), and severe tropical storm (STS) were concentrated between 300 and 500 hPa, while those for typhoon (TY), severe typhoon (STY), and Super TY varied from 200 to 350 hPa. Analyses of the cloud macro-/micro-physical properties showed that the top of TC cloud systems mainly consisted of ice clouds. For TCs of all intensities, areas near the TC center showed lower cloud-top pressures and lower cloud-top temperatures, more cloud fractions, and larger ice-cloud effective diameters. With the increase in TC intensity, the levels of ice clouds around the TC center became higher and the spiral cloud-rain bands became larger. When a TC developed into a TY, STY, or Super TY, the convection in the clouds was stronger, releasing more heat, thus forming a much warmer warm core.
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37

Patra, Sudhansu Shekhar. "Energy-Efficient Task Consolidation for Cloud Data Center." International Journal of Cloud Applications and Computing 8, no. 1 (January 2018): 117–42. http://dx.doi.org/10.4018/ijcac.2018010106.

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Energy saving in a Cloud Computing environment is a multidimensional challenge, which can directly decrease the in-use costs and carbon dioxide emission, while raising the system consistency. The process of maximizing the cloud computing resource utilization which brings many benefits such as better use of resources, rationalization of maintenance, IT service customization, QoS and reliable services, etc., is known as task consolidation. This article suggests the energy saving with task consolidation, by minimizing the number of unused resources in a cloud computing environment. In this article, various task consolidation algorithms such as MinIncreaseinEnergy, MaxUtilECTC, NoIdleMachineECTC, and NoIdleMachineMaxUtil are presented aims to optimize energy consumption of cloud data center. The outcomes have shown that the suggested algorithms surpass the existing ECTC and FCFSMaxUtil, MaxMaxUtil algorithms in terms of the CPU utilization and energy consumption.
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38

Tan, Xiao Long, Wen Bin Wang, and Yu Qin Yao. "Research of Network Virtualization in Data Center." Applied Mechanics and Materials 644-650 (September 2014): 2961–64. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2961.

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With the rapid grow of the volume of data and internet application, as an efficient and promising infrastructure, data center has been widely deployed .data center provide a variety of perform for network services, applications such as video stream, cloud compute and so on. All this services and applications call for volume, compute, bandwidth, and latency. Existing data centers lacks enough flexible so they provide poor support in QOS, deployability, manageability, and defense when facing attacks. Virtualized data centers are a good solution to these problems. Compared to existing data centers, virtualized data centers do better in resource utilization, scalability, and flexibility.
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39

Rivankar, Amey, and Anusooya G. "ENERGY EFFICIENT LOAD BALANCING FOR CLOUD DATA CENTER." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 162. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19604.

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Cloud computing is the latest trend in large-scale distributed computing. It provides diverse services on demand to distributive resources such asservers, software, and databases. One of the challenging problems in cloud data centers is to manage the load of different reconfigurable virtual machines over one another. Thus, in the near future of cloud computing field, providing a mechanism for efficient resource management will be very significant. Many load balancing algorithms have been already implemented and executed to manage the resources efficiently and adequately. The objective of this paper is to analyze shortcomings of existing algorithms and implement a new algorithm which will give optimized load balancingresult.
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40

Cox, Christopher J., Penny M. Rowe, Steven P. Neshyba, and Von P. Walden. "A synthetic data set of high-spectral-resolution infrared spectra for the Arctic atmosphere." Earth System Science Data 8, no. 1 (May 12, 2016): 199–211. http://dx.doi.org/10.5194/essd-8-199-2016.

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Abstract. Cloud microphysical and macrophysical properties are critical for understanding the role of clouds in climate. These properties are commonly retrieved from ground-based and satellite-based infrared remote sensing instruments. However, retrieval uncertainties are difficult to quantify without a standard for comparison. This is particularly true over the polar regions, where surface-based data for a cloud climatology are sparse, yet clouds represent a major source of uncertainty in weather and climate models. We describe a synthetic high-spectral-resolution infrared data set that is designed to facilitate validation and development of cloud retrieval algorithms for surface- and satellite-based remote sensing instruments. Since the data set is calculated using pre-defined cloudy atmospheres, the properties of the cloud and atmospheric state are known a priori. The atmospheric state used for the simulations is drawn from radiosonde measurements made at the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska (71.325° N, 156.615° W), a location that is generally representative of the western Arctic. The cloud properties for each simulation are selected from statistical distributions derived from past field measurements. Upwelling (at 60 km) and downwelling (at the surface) infrared spectra are simulated for 260 cloudy cases from 50 to 3000 cm−1 (3.3 to 200 µm) at monochromatic (line-by-line) resolution at a spacing of ∼ 0.01 cm−1 using the Line-by-line Radiative Transfer Model (LBLRTM) and the discrete-ordinate-method radiative transfer code (DISORT). These spectra are freely available for interested researchers from the NSF Arctic Data Center data repository (doi:10.5065/D61J97TT).
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41

Sharma, Akshay. "Cloud Computing: Energy Efficiency for Data Center Resources, Architectural Elements and Open Challenges." International Journal of Scientific Research 3, no. 8 (June 1, 2012): 144–47. http://dx.doi.org/10.15373/22778179/august2014/41.

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42

Zhang, Wei, Qinming Qi, and Jing Deng. "Building Intelligent Transportation Cloud Data Center Based on SOA." International Journal of Ambient Computing and Intelligence 8, no. 2 (April 2017): 1–11. http://dx.doi.org/10.4018/ijaci.2017040101.

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This paper is targeted at issues including traditional stovepipe data center, low utilization of IT equipment and data resources as a result of rigid IT structure, high maintenance costs and high energy consumption in system operation. By taking Beijing Municipal Committee of Transport (BMCT)'s data center as an example, a way to establish distributed traffic cloud data center based on SOA (Service-Oriented Architecture) fused with cloud computing is introduced in this paper; in addition, network-aware energy conservation scheduling DENS (Data- center Energy-efficient Network-aware Scheduling) algorithm applied in cloud data center is put forward to realize the full utilization of all kinds of resources in the cloud data center. Experimental results also show the effectiveness of the proposed algorithm by comparing with traditional DENS algorithms.
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43

Nandigana, Vishal. "Multi-Scale Data Center Room for Computing and Storage." Indonesian Journal of Computer Science 10, no. 2 (October 30, 2021): 238–44. http://dx.doi.org/10.33022/ijcs.v10i2.3018.

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In this paper, a list of multi-scale data center system computers and data Storage devices for cloud computing and transfer of cloud computing data and storage and computations in a square 100 meters room is worked for AIDesign software for cloud computing data storage using the devices and computing using the same storage requirements and performing the transfer of data storage and data transfer and computations for AIDesign software to perform the operations, computing, storage and data transfer to enable milliseconds computing of design and analysis in the cloud computing storage facility room. AIDesign software is patented, and industries approved and commercially available over https://aidesign.today.
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44

Dodson, Dillon S., and Jennifer D. Small Griswold. "Droplet inhomogeneity in shallow cumuli: the effects of in-cloud location and aerosol number concentration." Atmospheric Chemistry and Physics 19, no. 11 (June 4, 2019): 7297–317. http://dx.doi.org/10.5194/acp-19-7297-2019.

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Abstract. Aerosol–cloud interactions are complex, including albedo and lifetime effects that cause modifications to cloud characteristics. With most cloud–aerosol interactions focused on the previously stated phenomena, there have been no in situ studies that focus explicitly on how aerosols can affect large-scale (centimeters to tens of meters) droplet inhomogeneities within clouds. This research therefore aims to gain a better understanding of how droplet inhomogeneities within cumulus clouds can be influenced by in-cloud droplet location (cloud edge vs. center) and the surrounding environmental aerosol number concentration. The pair-correlation function (PCF) is used to identify the magnitude of droplet inhomogeneity from data collected on board the Center for Interdisciplinary Remotely Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft, flown during the 2006 Gulf of Mexico Atmospheric Composition and Climate Study (GoMACCS). Time stamps (at 10−4 m spatial resolution) of cloud droplet arrival times were measured by the Artium Flight phase-Doppler interferometer (PDI). Using four complete days of data with 81 non-precipitating cloud penetrations organized into two flights of low-pollution (L1, L2) and high-pollution (H1, H2) data shows enhanced inhomogeneities near cloud edge as compared to cloud center for all four cases. Low-pollution clouds are shown to have enhanced overall inhomogeneity, with flight L2 being solely responsible for this enhanced inhomogeneity. Analysis suggests cloud age plays a larger role in the amount of inhomogeneity experienced than the aerosol number concentration, with dissipating clouds showing increased inhomogeneities as compared to growing or mature clouds. Results using a single, vertically developed cumulus cloud demonstrate enhanced droplet inhomogeneity near cloud top as compared to cloud base.
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45

Qin, Peng, Wei Li, and Ke Ding. "A Big Data Security Architecture Based on Blockchain and Trusted Data Cloud Center." Wireless Communications and Mobile Computing 2022 (August 31, 2022): 1–8. http://dx.doi.org/10.1155/2022/7272405.

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In view of the shortcomings of big data security and privacy protection in cloud environment, a big data security architecture was proposed in this paper. Based on blockchain technology and trusted data cloud center, data security architecture adopts the ideas of trusted authentication, intrusion detection, data segmentation, and decentralized storage and applies Amazon AWS log processing service, PairHand user authentication protocol, and Hadoop data analysis framework to realize dig data security and privacy protection in the cloud environment. This paper realizes system initialization and user authentication, hierarchical data storage, decentralized storage, and user security access. The experimental results show that the system architecture can ensure data security and data access speed, which can provide necessary reference for cloud security.
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46

Shimpo, Akihiko, Masao Kanamitsu, Sam F. Iacobellis, and Song-You Hong. "Comparison of Four Cloud Schemes in Simulating the Seasonal Mean Field Forced by the Observed Sea Surface Temperature." Monthly Weather Review 136, no. 7 (July 1, 2008): 2557–75. http://dx.doi.org/10.1175/2007mwr2179.1.

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Abstract The impacts of four stratiform cloud parameterizations on seasonal mean fields are investigated using the global version of the Experimental Climate Prediction Center (ECPC) global-to-regional forecast system (G-RSM). The simulated fields are compared with the International Satellite Cloud Climatology Project (ISCCP) data for clouds, the Global Precipitation Climatology Project data for precipitation, the Earth Radiation Budget Experiment and the Surface Radiation Budget data for radiation, and the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (R-2) for temperature. Compared to observations, no stratiform cloud parameterization performed better in simulating all aspects of clouds, temperature, precipitation, and radiation fluxes. There are strong interactions between parameterized stratiform clouds and boundary layer clouds and convection, resulting in changes in low-level cloudiness and precipitation in the simulations. When the simulations are compared with ISCCP cloudiness and cloud water, and the NCEP/DOE R-2 relative humidity, the cloud amounts simulated by all four cloud schemes depend mostly on relative humidity with less dependency on the model’s cloud water, while the observed cloud amount is more strongly dependent on cloud water than relative humidity, suggesting that cloud parameterizations and the simulation of cloud water require further improvement.
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47

Digra, Lakshmi, and Sharanjeet Singh. "Survey on Energy Efficiency in Cloud Computing." Asian Journal of Computer Science and Technology 8, no. 1 (February 5, 2019): 18–21. http://dx.doi.org/10.51983/ajcst-2019.8.1.2125.

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Data centers are serious, energy-hungry infrastructures that can run large scale Internet based services. Energy ingesting representations are essential in designing and improving energy-efficient operations to reduce excessive energy consumption in data centers. This paper presents a survey on Energy efficiency in data centers, importance of energy efficiency. It also describes the increasing demands for data center in worldwide and the reasons for data centers energy inefficient? In this paper we define the challenges for implementing changes in data centers and explain why and how the energy requirements of data centers are growing. After that we compare the German data center market at international level and we see the energy consumption of data centers and servers in Germany from 2010 -2016.
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48

Jaiganesh, M., and A. Vincent Antony Kumar. "B3: Fuzzy-Based Data Center Load Optimization in Cloud Computing." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/612182.

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Cloud computing started a new era in getting variety of information puddles through various internet connections by any connective devices. It provides pay and use method for grasping the services by the clients. Data center is a sophisticated high definition server, which runs applications virtually in cloud computing. It moves the application, services, and data to a large data center. Data center provides more service level, which covers maximum of users. In order to find the overall load efficiency, the utilization service in data center is a definite task. Hence, we propose a novel method to find the efficiency of the data center in cloud computing. The goal is to optimize date center utilization in terms of three big factors—Bandwidth, Memory, and Central Processing Unit (CPU) cycle. We constructed a fuzzy expert system model to obtain maximum Data Center Load Efficiency (DCLE) in cloud computing environments. The advantage of the proposed system lies in DCLE computing. While computing, it allows regular evaluation of services to any number of clients. This approach indicates that the current cloud needs an order of magnitude in data center management to be used in next generation computing.
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49

Rajalakshmi, N. R., and N. Balaji. "Managing Load Balanced Machine in Cloud Data Center." Asian Journal of Research in Social Sciences and Humanities 6, no. 9 (2016): 272. http://dx.doi.org/10.5958/2249-7315.2016.00795.4.

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50

Qin, Hang, and Li Zhu. "Subject Oriented Autonomic Cloud Data Center Networks Model." Journal of Data Analysis and Information Processing 05, no. 03 (2017): 87–95. http://dx.doi.org/10.4236/jdaip.2017.53007.

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