Relevant bibliographies by topics / Data Cloud center

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Cloud center'

Author: Grafiati
Published: 27 April 2023

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Journal articles on the topic "Data Cloud center"

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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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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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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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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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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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Dissertations / Theses on the topic "Data Cloud center"

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Sergejev, Ivan. "Exposing the Data Center." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/51838.

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Given the rapid growth in the importance of the Internet, data centers - the buildings that store information on the web - are quickly becoming the most critical infrastructural objects in the world. However, so far they have received very little, if any, architectural attention. This thesis proclaims data centers to be the 'churches' of the digital society and proposes a new type of a publicly accessible data center. The thesis starts with a brief overview of the history of data centers and the Internet in general, leading to a manifesto for making data centers into public facilities with an architecture of their own. After, the paper proposes a roadmap for the possible future development of the building type with suggestions for placing future data centers in urban environments, incorporating public programs as a part of the building program, and optimizing the inside workings of a typical data center. The final part of the work, concentrates on a design for an exemplary new data center, buildable with currently available technologies. This thesis aims to: 1) change the public perception of the internet as a non-physical thing, and data centers as purely functional infrastructural objects without any deeper cultural significance and 2) propose a new architectural language for the type.
Master of Architecture
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Zhuang, Hao. "Performance Evaluation of Virtualization in Cloud Data Center." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104206.

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Amazon Elastic Compute Cloud (EC2) has been adopted by a large number of small and medium enterprises (SMEs), e.g. foursquare, Monster World, and Netflix, to provide various kinds of services. There has been some existing work in the current literature investigating the variation and unpredictability of cloud services. These work demonstrated interesting observations regarding cloud offerings. However, they failed to reveal the underlying essence of the various appearances for the cloud services. In this thesis, we looked into the underlying scheduling mechanisms, and hardware configurations, of Amazon EC2, and investigated their impact on the performance of virtual machine instances running atop. Specifically, several instances with the standard and high-CPU instance families are covered to shed light on the hardware upgrade and replacement of Amazon EC2. Then large instance from the standard family is selected to conduct focus analysis. To better understand the various behaviors of the instances, a local cluster environment is set up, which consists of two Intel Xeon servers, using different scheduling algorithms. Through a series of benchmark measurements, we observed the following findings: (1) Amazon utilizes highly diversified hardware to provision different instances. It results in significant performance variation, which can reach up to 30%. (2) Two different scheduling mechanisms were observed, one is similar to Simple Earliest Deadline Fist (SEDF) scheduler, whilst the other one analogies Credit scheduler in Xen hypervisor. These two scheduling mechanisms also arouse variations in performance. (3) By applying a simple "trial-and-failure" instance selection strategy, the cost saving is surprisingly significant. Given certain distribution of fast-instances and slow-instances, the achievable cost saving can reach 30%, which is attractive to SMEs which use Amazon EC2 platform.
Amazon Elastic Compute Cloud (EC2) har antagits av ett stort antal små och medelstora företag (SMB), t.ex. foursquare, Monster World, och Netflix, för att ge olika typer av tjänster. Det finns en del tidigare arbeten i den aktuella litteraturen som undersöker variationen och oförutsägbarheten av molntjänster. Dessa arbetenhar visat intressanta iakttagelser om molnerbjudanden, men de har misslyckats med att avslöja den underliggande kärnan hos de olika utseendena för molntjänster. I denna avhandling tittade vi på de underliggande schemaläggningsmekanismerna och maskinvarukonfigurationer i Amazon EC2, och undersökte deras inverkan på resultatet för de virtuella maskiners instanser som körs ovanpå. Närmare bestämt är det flera fall med standard- och hög-CPU instanser som omfattas att belysa uppgradering av hårdvara och utbyte av Amazon EC2. Stora instanser från standardfamiljen är valda för att genomföra en fokusanalys. För att bättre förstå olika beteenden av de olika instanserna har lokala kluster miljöer inrättas, dessa klustermiljöer består av två Intel Xeonservrar och har inrättats med hjälp av olika schemaläggningsalgoritmer. Genom en serie benchmarkmätningar observerade vi följande slutsatser: (1) Amazon använder mycket diversifierad hårdvara för att tillhandahållandet olika instanser. Från de olika instans-sub-typernas perspektiv leder hårdvarumångfald till betydande prestationsvariation som kan nå upp till 30%. (2) Två olika schemaläggningsmekanismer observerades, en liknande Simple Earliest Deadline Fist(SEDF) schemaläggare, medan den andra mer liknar Credit-schemaläggaren i Xenhypervisor. Dessa två schemaläggningsmekanismer ger även upphov till variationer i prestanda. (3) Genom att tillämpa en enkel "trial-and-failure" strategi för val av instans, är kostnadsbesparande förvånansvärt stor. Med tanke på fördelning av snabba och långsamma instanser kan kostnadsbesparingen uppgå till 30%, vilket är attraktivt för små och medelstora företag som använder Amazon EC2 plattform.
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Tudoran, Radu-Marius. "High-Performance Big Data Management Across Cloud Data Centers." Electronic Thesis or Diss., Rennes, École normale supérieure, 2014. http://www.theses.fr/2014ENSR0004.

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La puissance de calcul facilement accessible offerte par les infrastructures clouds, couplés à la révolution du "Big Data", augmentent l'échelle et la vitesse auxquelles l'analyse des données est effectuée. Les ressources de cloud computing pour le calcul et le stockage sont répartis entre plusieurs centres de données de par le monde. Permettre des transferts de données rapides devient particulièrement important dans le cadre d'applications scientifiques pour lesquels déplacer le traitement proche de données est coûteux voire impossible. Les principaux objectifs de cette thèse consistent à analyser comment les clouds peuvent devenir "Big Data - friendly", et quelles sont les meilleures options pour fournir des services de gestion de données aptes à répondre aux besoins des applications. Dans cette thèse, nous présentons nos contributions pour améliorer la performance de la gestion de données pour les applications exécutées sur plusieurs centres de données géographiquement distribués. Nous commençons avec les aspects concernant l'échelle du traitement de données sur un site, et poursuivons avec le développements de solutions de type MapReduce permettant la distribution des calculs entre plusieurs centres. Ensuite, nous présentons une architecture de service de transfert qui permet d'optimiser le rapport coût-performance des transferts. Ce service est exploité dans le contexte de la diffusion de données en temps-réel entre des centres de données de clouds. Enfin, nous étudions la viabilité, pour une fournisseur de cloud, de la solution consistant à intégrer cette architecture comme un service basé sur un paradigme de tarification flexible, qualifiée de "Transfert-as-a-Service"
The easily accessible computing power offered by cloud infrastructures, coupled with the "Big Data" revolution, are increasing the scale and speed at which data analysis is performed. Cloud computing resources for compute and storage are spread across multiple data centers around the world. Enabling fast data transfers becomes especially important in scientific applications where moving the processing close to data is expensive or even impossible. The main objectives of this thesis are to analyze how clouds can become "Big Data - friendly", and what are the best options to provide data management services able to meet the needs of applications. In this thesis, we present our contributions to improve the performance of data management for applications running on several geographically distributed data centers. We start with aspects concerning the scale of data processing on a site, and continue with the development of MapReduce type solutions allowing the distribution of calculations between several centers. Then, we present a transfer service architecture that optimizes the cost-performance ratio of transfers. This service is operated in the context of real-time data streaming between cloud data centers. Finally, we study the viability, for a cloud provider, of the solution consisting in integrating this architecture as a service based on a flexible pricing paradigm, qualified as "Transfer-as-a-Service"
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de, Carvalho Tiago Filipe Rodrigues. "Integrated Approach to Dynamic and Distributed Cloud Data Center Management." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/739.

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Management solutions for current and future Infrastructure-as-a-Service (IaaS) Data Centers (DCs) face complex challenges. First, DCs are now very large infrastructures holding hundreds of thousands if not millions of servers and applications. Second, DCs are highly heterogeneous. DC infrastructures consist of servers and network devices with different capabilities from various vendors and different generations. Cloud applications are owned by different tenants and have different characteristics and requirements. Third, most DC elements are highly dynamic. Applications can change over time. During their lifetime, their logical architectures evolve and change according to workload and resource requirements. Failures and bursty resource demand can lead to unstable states affecting a large number of services. Global and centralized approaches limit scalability and are not suitable for large dynamic DC environments with multiple tenants with different application requirements. We propose a novel fully distributed and dynamic management paradigm for highly diverse and volatile DC environments. We develop LAMA, a novel framework for managing large scale cloud infrastructures based on a multi-agent system (MAS). Provider agents collaborate to advertise and manage available resources, while app agents provide integrated and customized application management. Distributing management tasks allows LAMA to scale naturally. Integrated approach improves its efficiency. The proximity to the application and knowledge of the DC environment allow agents to quickly react to changes in performance and to pre-plan for potential failures. We implement and deploy LAMA in a testbed server cluster. We demonstrate how LAMA improves scalability of management tasks such as provisioning and monitoring. We evaluate LAMA in light of state-of-the-art open source frameworks. LAMA enables customized dynamic management strategies to multi-tier applications. These strategies can be configured to respond to failures and workload changes within the limits of the desired SLA for each application.
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Raad, Patrick. "Protocol architecture and algorithms for distributed data center networks." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066571/document.

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De nos jours les données ainsi que les applications dans le nuage (cloud) connaissent une forte croissance, ce qui pousse les fournisseurs à chercher des solutions garantissant un lien réseau stable et résilient à leurs utilisateurs. Dans cette thèse on étudie les protocoles réseaux et les stratégies de communication dans un environnement de centre de données distribués. On propose une architecture cloud distribuée, centrée sur l’utilisateur et qui a pour but de: (i) migrer des machines virtuelles entre les centres de données avec un temps d’indisponibilité faible; (ii) fournir un accès résilient aux machines virtuelles; (iii) minimiser le délai d'accès au cloud. On a identifié deux problèmes de décision: le problème d'orchestration de machines virtuelles, prenant en compte la mobilité des utilisateurs, et le problème de basculement et de configuration des localisateurs, prenant en compte les états des liens inter- et intra-centre de données. On évalue notre architecture en utilisant une plate-forme de test avec des centres de données distribués géographiquement et en simulant des scenarios basés sur des traces de mobilités réelles. On montre que, grâce à quelques modifications apportées aux protocoles d'overlay, on peut avoir des temps d'indisponibilité très faibles pendant la migration de machines virtuelles entre deux centres de données. Puis on montre qu’en reliant la mobilité des machines virtuelles aux déplacement géographiques des utilisateurs, on peut augmenter le débit de la connexion. De plus, quand l’objectif est de maximiser le débit entre l’utilisateur et sa ressource, on démontre par des simulations que la décision de l'emplacement des machines virtuelles est plus importante que la décision de basculement de point d'entrée du centre de données. Enfin, grâce à un protocole de transport multi-chemins, on montre comment optimiser les performances de notre architecture et comment à partir des solutions de routage intra-centre de données on peut piloter le basculement des localisateurs
While many business and personal applications are being pushed to the cloud, offering a reliable and a stable network connectivity to cloud-hosted services becomes an important challenge to face in future networks. In this dissertation, we design advanced network protocols, algorithms and communication strategies to cope with this evolution in distributed data center architectures. We propose a user-centric distributed cloud network architecture that is able to: (i) migrate virtual resources between data centers with an optimized service downtime; (ii) offer resilient access to virtual resources; (iii) minimize the cloud access latency. We identify two main decision making problems: the virtual machine orchestration problem, also taking care of user mobility, and the routing locator switching configuration problem, taking care of both extra and intra data center link states. We evaluate our architecture using real test beds of geographically distributed data centers, and we also simulate realistic scenarios based on real mobility traces. We show that migrating virtual machines between data centers at negligible downtime is possible by enhancing overlay protocols. We then demonstrate that by linking cloud virtual resource mobility to user mobility we can get a considerable gain in the transfer rates. We prove by simulations using real traces that the virtual machine placement decision is more important than the routing locator switching decision problem when the goal is to increase the connection throughput: the cloud access performance is primarily affected by the former decision, while the latter decision can be left to intra data center traffic engineering solutions. Finally, we propose solutions to take profit from multipath transport protocols for accelerating cloud access performance in our architecture, and to let link-state intra data center routing fabrics piloting the cloud access routing locator switching
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Pipkin, Everest R. "It Was Raining in the Data Center." Research Showcase @ CMU, 2018. http://repository.cmu.edu/theses/138.

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Stemming from a 2011 incident inside of a Facebook data facility in which hyper-cooled air formed a literal (if somewhat transient) rain cloud in the stacks, It was raining in the data center examines ideas of non-places and supermodernity applied to contemporary network infrastructure. It was raining in the data center argues that the problem of the rain cloud is as much a problem of psychology as it is a problem of engineering. Although humidity-management is a predictable snag for any data center, the cloud was a surprise; a self-inflicted side-effect of a strategy of distance. The rain cloud was a result of the same rhetoric of ephemerality that makes it easy to imagine the inside of a data center to be both everywhere and nowhere. This conceit of internet data being placeless shares roots with Marc Augé’s idea of non-places (airports, highways, malls), which are predicated on the qualities of excess and movement. Without long-term inhabitants, these places fail to tether themselves to their locations, instead existing as a markers of everywhere. Such a premise allows the internet to exist as an other-space that is not conceptually beholden to the demands of energy and landscape. It also liberates the idea of ‘the network’ from a similar history of industry. However, the network is deeply rooted in place, as well as in industry and transit. Examining the prevalence of network overlap in American fiber-optic cabling, it becomes easy to trace routes of cables along major US freight train lines and the US interstate highway system. The historical origin of this network technology is in weaponization and defense, from highways as a nuclear-readiness response to ARPANET’s Pentagon-based funding. Such a linkage with the military continues today, with data centers likely to be situated near military installations— sharing similar needs electricity, network connectivity, fair climate, space, and invisibility. We see the repetition of militarized tropes across data structures. Fiber-optic network locations are kept secret; servers are housed in cold-war bunkers; data centers nest next to military black-sites. Similarly, Augé reminds us that non-places are a particular target of terrorism, populated as they are with cars, trains, drugs and planes that turn into weapons. When the network itself is at threat of weaponization, the effect is an ambient and ephemeral fear; a paranoia made of over-connection.
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Mahmud, A. S. M. Hasan. "Sustainable Resource Management for Cloud Data Centers." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2634.

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In recent years, the demand for data center computing has increased significantly due to the growing popularity of cloud applications and Internet-based services. Today's large data centers host hundreds of thousands of servers and the peak power rating of a single data center may even exceed 100MW. The combined electricity consumption of global data centers accounts for about 3% of worldwide production, raising serious concerns about their carbon footprint. The utility providers and governments are consistently pressuring data center operators to reduce their carbon footprint and energy consumption. While these operators (e.g., Apple, Facebook, and Google) have taken steps to reduce their carbon footprints (e.g., by installing on-site/off-site renewable energy facility), they are aggressively looking for new approaches that do not require expensive hardware installation or modification. This dissertation focuses on developing algorithms and systems to improve the sustainability in data centers without incurring significant additional operational or setup costs. In the first part, we propose a provably-efficient resource management solution for a self-managed data center to cap and reduce the carbon emission while maintaining satisfactory service performance. Our solution reduces the carbon emission of a self-managed data center to net-zero level and achieves carbon neutrality. In the second part, we consider minimizing the carbon emission in a hybrid data center infrastructure that includes geographically distributed self-managed and colocation data centers. This segment identifies and addresses the challenges of resource management in a hybrid data center infrastructure and proposes an efficient distributed solution to optimize the workload and resource allocation jointly in both self-managed and colocation data centers. In the final part, we explore sustainable resource management from cloud service users' point of view. A cloud service user purchases computing resources (e.g., virtual machines) from the service provider and does not have direct control over the carbon emission of the service provider's data center. Our proposed solution encourages a user to take part in sustainable (both economical and environmental) computing by limiting its spending on cloud resource purchase while satisfying its application performance requirements.
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Li, Dawei. "On the Design and Analysis of Cloud Data Center Network Architectures." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/413608.

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Computer and Information Science
Ph.D.
Cloud computing has become pervasive in the IT world, as well as in our daily lives. The underlying infrastructures for cloud computing are the cloud data centers. The Data Center Network (DCN) defines what networking devices are used and how different devices are interconnected in a cloud data center; thus, it has great impacts on the total cost, performances, and power consumption of the entire data center. Conventional DCNs use tree-based architectures, where a limited number of high-end switches and high-bandwidth links are used at the core and aggregation levels to provide required bandwidth capacity. A conventional DCN often suffers from high expenses and low fault-tolerance, because high-end switches are expensive and a failure of such a high-end switch will result in disastrous consequences in the network. To avoid the problems and drawbacks in conventional DCNs, recent works adopt an important design principle: using Commodity-Off-The-Shelf (COTS) cheap switches to scale out data centers to large sizes, instead of using high-end switches to scale up data centers. Based on this scale-out principle, a large number of novel DCN architectures have been proposed. These DCN architectures are classified into two categories: switch-centric and server-centric DCN architectures. In both switch-centric and server-centric architectures, COTS switches are used to scale out the network to a large size. In switch-centric DCNs, routing intelligence is placed on switches; each server usually uses only one port of the Network Interface Card (NIC) to connect to the switches. In server-centric DCNs, switches are only used as dummy cross-bars; servers in the network serve as both computation nodes and packet forwarding nodes that connect switches and other servers, and routing intelligence is placed on servers, where multiple NIC ports may be used. This dissertation considers two fundamental problems in designing DCN architectures using the scale-out principle. The first problem considers how to maximize the total number of dual-port servers in a server-centric DCN given a network diameter constraint. Motivated by the Moore Bound, which provides the upper bound on the number of nodes in a traditional graph given a node degree and diameter, we give an upper bound on the maximum number of dual-port servers in a DCN, given a network diameter constraint and a switch port number. Then, we propose three novel DCN architectures, SWCube, SWKautz, and SWdBruijn, whose numbers of servers are close to the upper bound, and are larger than existing DCN architectures in most cases. SWCube is based on the generalized hypercube. SWCube accommodates a comparable number of servers to that of DPillar, which is the largest existing one prior to our work. SWKautz and SWdBruijn are based on the Kautz graph and the de Bruijn graph, respectively. They always accommodate more servers than DPillar. We investigate various properties of SWCube, SWKautz, and SWdBruijn; we also compare them with various existing DCN architectures and demonstrate their advantages over existing architectures. The second problem focuses on the tradeoffs between network performances and power consumption in designing DCN architectures. We have two motivations for our work. The first one is that most existing works take extreme designs in terms of improving network performances and reducing the power consumption. Some DCNs use too many networking devices to improve the performances; their power consumption is very high. Other DCNs use two few networking devices, and their performances are very poor. We are interested in exploring the quantitative tradeoffs between network performances and power consumption in designing DCN architectures. The second motivation is that there do not exist important unified performance and power consumption metrics for general DCNs. Thus, we propose two important unified performance and power consumption metrics. Then, we propose three novel DCN architectures that achieve important tradeoff points in the design spectrum: FCell, FSquare, and FRectangle. Besides, we find that in all these three new architectures, routing intelligence can be placed on both servers and switches; thus they enjoy the advantages of both switch-centric and server-centric architectures, and can be regarded as a new category of DCN architectures, the dual-centric DCN architectures. We also investigate various other properties for our proposed architectures and verify that they are excellent candidates for practical cloud data centers.
Temple University--Theses
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Soares, Maria José. "Data center - a importância de uma arquitectura." Master's thesis, Universidade de Évora, 2011. http://hdl.handle.net/10174/11604.

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Este trabalho, apresenta um estudo sob a forma de overview, abordando a temática dos Data Centers no que concerne à importância da sua arquitectura. Foram elencados os principais factores críticos a considerar numa arquitectura, bem como as melhores práticas a implementar no sentido de se avaliar a importância de uma certificação pela entidade de certificação - Uptime Institute. Aborda-se ainda o eventual interesse em como expandir essa certificação/qualificação aos recursos humanos, como garantia de qualidade de serviços e estratégia de marketing. Como forma de consubstanciar a temática, foi criado um Case Study, observando-se um universo de sete Data Centers em Portugal, pertencentes ao sector público e privado, permitindo a verificação e comparação de boas práticas, bem como os aspectos menos positivos a considerar dentro da área. Finalmente, são deixadas algumas reflexões sobre o que pode ser a tendência de evolução dos Data Centers numa perspectiva de qualidade; ### Abstract: This is presents a study, in the form of overview, addressing the issue of the importance of architecture in Data Centers. The main critical factors in architecture were considered as well as the best practices to implement it in order to assess the value of a recognized certificate. It also discusses the possible interest in expanding the certification/qualification of human resources as a guarantee for quality of the services provided and marketing strategies. To support this work we analyzed seven Case Studies, where it was possible to observe a representative universe of Data Centers in Portugal, belonging to the public and private sectors, allowing the verification and comparison of good practices as well as the less positive aspects to consider within this area. At the end of the document we present conclusions on what may be the trend for the evolution of Data Center as far as quality is concerned.
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Izumo, Naoki. "Clouded space: Internet physicality." Thesis, University of Iowa, 2017. https://ir.uiowa.edu/etd/5515.

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On Friday October 21st, 2016, there was a large-scale hack of an Internet domain hosting provider that took several websites including Netflix, Amazon, Reddit, and Twitter offline. Dyn, a cloud-based Internet Performance Management company, announced at 9:20AM ET that it resolved an attack that began at 7AM ET that day. However, another attack happened at 11:52AM ET. The attacks raised concern among the public and directed our attention towards Internet security. This also revealed the precariousness of Internet infrastructure. The infrastructure being used today is opaque, unregulated, and incontestable. Municipally provided public utilities are built without any transparency; thus, we do not expect failure from those systems. For instance, the Flint, Michigan water crisis raised issues of water infrastructure. Not only did the crisis spark talks about the corrosion of pipes, but also larger societal issues. Flint, a poor, largely African American community, became a victim of environmental racism—a type of discrimination where communities of color or low-income residents are forced to live in environmental dangerous areas. In order for myself and the larger public to understand this opaque system, we need to understand the infrastructure and how it works. With regards to Internet infrastructure, I focus on data centers, where there are backup servers, batteries and generators built into the architectural landscape in case of failure. There is a common held thought that overshadows the possibility of imminent technological failure—it cannot happen. This sort of thinking influences other modes of our daily lives: individuals building concrete bomb shelters underground for the apocalypse, stocking food, but not preparing for data breakdown. The consciousness of loss is further perpetuated by technology and its life expectancy. Clouded Space: Internet Physicality attempts to explore the unexceptional infrastructure of the Internet and how it exists right beneath our feet. That in itself is not very cloud-like. The work questions integrity of our infrastructure as much as environmental issues, highlighting the questionable relationship we have with data and our inclination to backup data to protect ourselves from failure. This is a relatively new topic and the challenges are not well understood. There seem to be cracks in the foundation, and though they are not yet obvious, they appear to be widening.
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Books on the topic "Data Cloud center"

1

Zhang, Lei, and Le Chen. Cloud Data Center Network Architectures and Technologies. First edition. | Boca Raton : CRC Press, 2021. | Summary: “This book has: CRC Press, 2021. http://dx.doi.org/10.1201/9781003143185.

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Malcolm, Orr, and Page Greg, eds. Cloud computing: Automating the virtualized data center. Indianapolis, IN: Cisco Press, 2012.

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Tsai, Linjiun, and Wanjiun Liao. Virtualized Cloud Data Center Networks: Issues in Resource Management. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32632-0.

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1960-, Franklin Curtis, ed. Cloud computing: Technologies and strategies of the ubiquitous data center. New York: CRC, 2010.

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Chee, Brian J. S. Yun ji suan: Wu chu bu zai de shu ju zhong xin = Cloud computing ; technologies and strategies of the ubiquitous data center. Beijing: Guo fang gong ye chu ban she, 2013.

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International Business Machines Corporation. International Technical Support Organization, ed. Managing security and compliance in cloud or virtualized data centers. [Poughkeepsie, NY: IBM Corp., International Technical Support Organization], 2013.

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K, Kokula Krishna Hari, ed. An Efficient Load Balancing Algorithm for virtualized Cloud Data Centers: ICCCEG 2014. Vietnam: Association of Scientists, Developers and Faculties, 2014.

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Beard, Haley. Cloud computing best practices: For managing and measuring processes for on-demand computing, applications and data centers in the cloud with SLAs. Brisbane, Australia: Art of Service, 2008.

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United States. National Aeronautics and Space Administration., ed. Reduction and analysis of seasons 15 and 16 (1991-1992), Pioneer Venus radio occultation data and correlative studies with observations of the near infra-red emission of Venus: Report to the National Aeronautics and Space Administration, Ames Research Center for grant NCC2-753, April 1, 1992 through May 31, 1995. [Washington, DC: National Aeronautics and Space Administration, 1995.

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Green computing: Tools and techniques for saving energy, money, and resources. Boca Raton: CRC Press, 2014.

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Book chapters on the topic "Data Cloud center"

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Comer, Douglas E. "Data Center Infrastructure And Equipment." In The Cloud Computing Book, 37–52. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003147503-6.

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Cherkaoui, Omar, and Ramesh Menon. "Virtualization, Cloud, SDN, and SDDC in Data Centers." In Data Center Handbook, 389–400. Hoboken, NJ: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118937563.ch20.

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Zheng, Jianping, Yue Sun, and Wenhui Zhou. "Cloud Computing Based Internet Data Center." In Lecture Notes in Computer Science, 700–704. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10665-1_75.

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Qi, Jiaju, and Long Zhao. "Data Center Network in Cloud Computing." In Encyclopedia of Wireless Networks, 260–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_322.

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Qi, Jiaju, and Long Zhao. "Data Center Network in Cloud Computing." In Encyclopedia of Wireless Networks, 1–4. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-32903-1_322-1.

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Tsai, Linjiun, and Wanjiun Liao. "Transformation of Data Center Networks." In Virtualized Cloud Data Center Networks: Issues in Resource Management., 15–27. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32632-0_3.

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Lin, Xiuyan, Zhanghui Liu, and Wenzhong Guo. "Energy-Efficient VM Placement Algorithms for Cloud Data Center." In Cloud Computing and Big Data, 42–54. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28430-9_4.

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Kantarci, B., and H. T. Mouftah. "Inter-Data-Center Networks with Minimum Operational Costs." In Cloud Services, Networking, and Management, 105–28. Hoboken, NJ: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781119042655.ch5.

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Svoboda, Tomas, and Josef Horalek. "Utilization of NFV in Cloud Data Center." In Lecture Notes in Electrical Engineering, 156–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75605-9_22.

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Alhebaishi, Nawaf, Lingyu Wang, Sushil Jajodia, and Anoop Singhal. "Threat Modeling for Cloud Data Center Infrastructures." In Foundations and Practice of Security, 302–19. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51966-1_20.

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Conference papers on the topic "Data Cloud center"

1

Sengupta, Sudipta. "Cloud data center networks." In the ACM SIGMETRICS joint international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1993744.1993782.

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Sydney, Ali, Abdul Alim, Chris Ward, Claude Basso, and Bengi Karacali. "Cloud Data Center Fabric Virtualization." In 2022 IEEE 15th International Conference on Cloud Computing (CLOUD). IEEE, 2022. http://dx.doi.org/10.1109/cloud55607.2022.00048.

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Fiandrino, Claudio, Dzmitry Kliazovich, Pascal Bouvry, and Albert Y. Zomaya. "Performance Metrics for Data Center Communication Systems." In 2015 IEEE 8th International Conference on Cloud Computing (CLOUD). IEEE, 2015. http://dx.doi.org/10.1109/cloud.2015.23.

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Huang, Lei, Qin Jia, Xin Wang, Shuang Yang, and Baochun Li. "PCube: Improving Power Efficiency in Data Center Networks." In 2011 IEEE 4th International Conference on Cloud Computing (CLOUD). IEEE, 2011. http://dx.doi.org/10.1109/cloud.2011.74.

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Mengistu, Tessema, Abdulrahman Alahmadi, Abdullah Albuali, Yousef Alsenani, and Dunren Che. "A "No Data Center" Solution to Cloud Computing." In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). IEEE, 2017. http://dx.doi.org/10.1109/cloud.2017.99.

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S K, Shravan, J. Lakshmi, and Neeraj Bisht. "Towards Improving Data Center Utilisation by Reducing Fragmentation." In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). IEEE, 2018. http://dx.doi.org/10.1109/cloud.2018.00141.

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Hu, Yan, Ming Zhu, Yong Xia, Kai Chen, and Yanlin Luo. "GARDEN: Generic Addressing and Routing for Data Center Networks." In 2012 IEEE 5th International Conference on Cloud Computing (CLOUD). IEEE, 2012. http://dx.doi.org/10.1109/cloud.2012.9.

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Yuan, Haitao, Jing Bi, Jia Zhang, Wei Tan, and Keman Huang. "Workload-Aware Revenue Maximization in SDN-Enabled Data Center." In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). IEEE, 2017. http://dx.doi.org/10.1109/cloud.2017.12.

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Saed, Kamariah Abu, Norshakirah Aziz, Ade Wahyu Ramadhani, and Noor Hafizah Hassan. "Data Governance Cloud Security Assessment at Data Center." In 2018 4th International Conference on Computer and Information Sciences (ICCOINS). IEEE, 2018. http://dx.doi.org/10.1109/iccoins.2018.8510612.

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Dutreilh, Xavier, Aurélien Moreau, Jacques Malenfant, Nicolas Rivierre, and Isis Truck. "From Data Center Resource Allocation to Control Theory and Back." In 2010 IEEE International Conference on Cloud Computing (CLOUD). IEEE, 2010. http://dx.doi.org/10.1109/cloud.2010.55.

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Reports on the topic "Data Cloud center"

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DEFENSE BUSINESS BOARD WASHINGTON DC. DoD Information Technology Modernization: A Recommended Approach to Data Center Consolidation and Cloud Computing. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada563977.

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Gurieiev, Viktor, Yulii Kutsan, Anna Iatsyshyn, Andrii Iatsyshyn, Valeriia Kovach, Evgen Lysenko, Volodymyr Artemchuk, and Oleksandr Popov. Simulating Systems for Advanced Training and Professional Development of Energy Specialists in Power Sector. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4456.

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The crisis of the system of professional development and personnel training in the energy sector exists not only in Ukraine but also all over the world. The article describes the concept of development and functioning of the industry system of personnel training in the energy sector of Ukraine. The importance of using modern web-oriented technologies to improve the skills of operational and dispatching personnel in the energy sector of Ukraine is substantiated. The meth- ods of distributed power system operating modes modelling are presented. De- velopment and software tools for the construction of distributed simulating sys- tems and particular features of cloud technologies application for the creation of a virtual training centers network in the energy sector, as well as the ways to automate the process of simulating scenarios development are described. The ex- perience of introducing remote training courses for energy specialists and remote web-based training simulators based on a comprehensive model of the energy system of Ukraine is presented. An important practical aspect of the research is the application of software and data support for the development of personnel key competencies in the energy sector for rapid recognition of accidents and, if necessary, accident management. This will allow them to acquire knowledge and practical skills to solve the problems of analysis, modelling, forecasting, and monitoring data visualization of large power systems operating modes.
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