Log in

Relevant bibliographies by topics / Cloud data centers / Journal articles

Journal articles on the topic 'Cloud data centers'

To see the other types of publications on this topic, follow the link: Cloud data centers.

Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Cloud data centers.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

Spillner, Josef, and Alan Sill. "Reengineering Cloud Data Centers." IEEE Cloud Computing 5, no. 6 (November 2018): 26–27. http://dx.doi.org/10.1109/mcc.2018.064181117.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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 â€œGreen Cloudâ€. 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.

APA, Harvard, Vancouver, ISO, and other styles

7

Shojafar, Mohammad, Zahra Pooranian, Mehdi Sookhak, and Rajkumar Buyya. "Recent advances in cloud data centers toward fog data centers." Concurrency and Computation: Practice and Experience 31, no. 8 (January 30, 2019): e5164. http://dx.doi.org/10.1002/cpe.5164.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

9

M N, Kavyasri, and Dr Ramesh B. "Key-Cipher-Policy based ABE with Efficient Encryption of Multimedia Data at Data Centers of Cloud." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 1 (May 30, 2022): 73–76. http://dx.doi.org/10.35940/ijrte.c6486.0511122.

Full text

Abstract:

Cloud computing is relatively one among the new technologies that is attracting moreclients to adopt cloud storage for easy and convenient online data storage and sharing. Because of its efficient computations, it has attracted the attention of both industry and academia. Companies began outsourcing confidential data to cloud service data centres. These data storage applications have security concerns about data confidentiality and privacy. When data is moved to the cloud, the customer loses control of the data and must rely on the cloud service provider. To protect their data, clients must first guarantee that it is encrypted. Encryption is a promising method for keeping data private in the cloud. ABE is a potential technique for dealing with security challenges in data center cloud storage. The key benefit is that it allows flexible one-to-many encryption. We present a model termed key-Cipher-policy-based ABE in this study. It allows optimized and more secure access to data stored at the data centers of cloud. with optimized encryption and less encryption time.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

11

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

12

Maltz, David A. "Challenges in cloud scale data centers." ACM SIGMETRICS Performance Evaluation Review 41, no. 1 (June 14, 2013): 3–4. http://dx.doi.org/10.1145/2494232.2465767.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

14

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

15

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

16

Tang, Lei, Zheng Ce Cai, Guo Long Chen, and Xian Wei Li. "Power Reduction Techniques in Cloud Data Centers." Advanced Materials Research 1061-1062 (December 2014): 1070–73. http://dx.doi.org/10.4028/www.scientific.net/amr.1061-1062.1070.

Full text

Abstract:

In recent years, cloud computing has received much attention from both academia and engineering areas. With more and more companies beginning to provide cloud services, more and more data centers are being built. Recent studies show that the energy consumed by cloud data centers accounts for a large fraction of the total power consumption today. This motivates us to survey power reduction techniques in cloud data centers.

APA, Harvard, Vancouver, ISO, and other styles

17

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

18

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

19

K L, Anitha, and T. R. Gopalakrishnan Nair. "Data storage lock algorithm with cryptographic techniques." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 3843. http://dx.doi.org/10.11591/ijece.v9i5.pp3843-3849.

Full text

Abstract:

<span>The cloud computing had its impact far and wide, and Enterprise solutions are getting migrated to different types of clouds. The services are delivered from the data centers which are located all over the world. As the data is roaming with less control in any data centers, data security issues in cloud are very challenging. Therefore we need multi-level authentication, data integrity, privacy and above all encryption to safeguard our data which is stored on to the cloud. The data and applications cannot be relocated to a virtual server without much degree of security concern as there can be much confidential data or mission-critical applications. In this paper, we propose Data Storage Lock Algorithm (DSLA) to store confidential data thereby provides secure data storage in cloud computing based on cryptographic standards.</span>

APA, Harvard, Vancouver, ISO, and other styles

20

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

21

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

22

Song, Hai Na, Xiao Qing Zhang, and Zhong Tang He. "Multidimensional Resource Scheduling in Cloud Data Centers." Advanced Materials Research 1008-1009 (August 2014): 1513–16. http://dx.doi.org/10.4028/www.scientific.net/amr.1008-1009.1513.

Full text

Abstract:

Cloud computing environment is regarded as a kind of multi-tenant computing mode. With virtulization as a support technology, cloud computing realizes the integration of multiple workloads in one server through the package and seperation of virtual machines. Aiming at the contradiction between the heterogeneous applications and uniform shared resource pool, using the idea of bin packing, the multidimensional resource scheduling problem is analyzed in this paper. We carry out some example analysis in one-dimensional resource scheduling, two-dimensional resource schduling and three-dimensional resource scheduling. The results shows that the resource utilization of cloud data centers will be improved greatly when the resource sheduling is conducted after reorganizing rationally the heterogeneous demands.

APA, Harvard, Vancouver, ISO, and other styles

23

Fedchenkov, Petr, Andrey Shevel, Sergey Khoruzhnikov, Oleg Sadov, Oleg Lazo, and Nikitta Samokhin. "The cloud of geographically distributed data centers." EPJ Web of Conferences 214 (2019): 07007. http://dx.doi.org/10.1051/epjconf/201921407007.

Full text

Abstract:

ITMO University (ifmo.ru) is developing the cloud of geographically distributed data centres. The geographically distributed means data centres (DC) located in different places far from each other by hundreds or thousands of kilometres. Usage of the geographically distributed data centres promises a number of advantages for end users such as opportunity to add additional DC and service availability through redundancy and geographical distribution. Services like data transfer, computing, and data storage are provided to users in the form of virtual objects including virtual machines, virtual storage, virtual data transfer link.

APA, Harvard, Vancouver, ISO, and other styles

24

Zharikov, Eduard, Sergii Telenyk, and Petro Bidyuk. "Adaptive Workload Forecasting in Cloud Data Centers." Journal of Grid Computing 18, no. 1 (November 29, 2019): 149–68. http://dx.doi.org/10.1007/s10723-019-09501-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Iliev, Rosen, and Kristina Ignatova. "Implementation of Cloud Technologies for Building Data Centers in Defence and Security." Information & Security: An International Journal 43, no. 1 (2019): 89–97. http://dx.doi.org/10.11610/isij.4308.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Shen, Lianteng, Shengpan Qian, Tianyi Zhai, Ling Li, and Zhe Li. "Research on cloud computing high-density data center infrastructure and environment matching technology." MATEC Web of Conferences 336 (2021): 02028. http://dx.doi.org/10.1051/matecconf/202133602028.

Full text

Abstract:

The current rapid development of cloud computing and networks has put forward new requirements for the construction of new infrastructure such as data centers. This paper compares traditional data centers and high-density data centers, proposes a three-tier infrastructure for high-density data centers, and analyzes the data center environment. To solve the obvious problem of software and hardware heterogeneity in high-density data centers, this paper uses virtualization technology to pool resources in high-density data centers and introduces SOA architecture to manage software and hardware resources hierarchically. Finally, the data center infrastructure and environment matching technology are studied.

APA, Harvard, Vancouver, ISO, and other styles

27

P, Naresh, Rajyalakshmi P, Krishna Vempati, and Saidulu D. "IMPROVING THE DATA TRANSMISSION SPEED IN CLOUD MIGRATION BY USING MAPREDUCE FOR BIGDATA." International Journal of Engineering Technology and Management Sciences 4, no. 5 (September 28, 2020): 73–75. http://dx.doi.org/10.46647/ijetms.2020.v04i05.013.

Full text

Abstract:

Cloud acts as a data storage and also used for data transfer from one cloud to other. Here data exchange takes place among cloud centers of organizations. At each cloud center huge amount of data was stored, which interns hard to store and retrieve information from it. While migrating the data there are some issues like low data transfer rate, end to end latency issues and data storage issues will occur. As data was distributed among so many cloud centers from single source, will reduces the speed of migration. In distributed cloud computing it is very difficult to transfer the data fast and securely. This paper explores MapReduce within the distributed cloud architecture where MapReduce assists at each cloud. It strengthens the data migration process with the help of HDFS. Compared to existing cloud migration approach the proposed approach gives accurate results interns of speed, time and efficiency.

APA, Harvard, Vancouver, ISO, and other styles

28

Soltane, Merzoug, Kazar Okba, Derdour Makhlouf, and Sean B. Eom. "Smart Configuration and Auto Allocation of Resource in Cloud Data Centers." International Journal of Business Analytics 5, no. 4 (October 2018): 1–23. http://dx.doi.org/10.4018/ijban.2018100101.

Full text

Abstract:

Cloud computing is one of emerging computing models that has many advantages. The IT industry is keenly aware of the need for Green Cloud computing solutions that save energy for the environment as well as reduce operational costs. This article presents a new green Cloud Computing framework based on multi agent systems for optimizing resource allocation in data centers (DCs). Our framework based on a new cloud computing architecture that benefits from the combination of the Cloud and agent technologies. DCs hosting Cloud applications need energy-aware resource allocation mechanisms that minimize energy costs and other operational costs. This article offers a logical solution to manage physical and virtual resources in smarter data center.

APA, Harvard, Vancouver, ISO, and other styles

29

Bai, Wei-Hua, Jian-Qing Xi, Jia-Xian Zhu, and Shao-Wei Huang. "Performance Analysis of Heterogeneous Data Centers in Cloud Computing Using a Complex Queuing Model." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/980945.

Full text

Abstract:

Performance evaluation of modern cloud data centers has attracted considerable research attention among both cloud providers and cloud customers. In this paper, we investigate the heterogeneity of modern data centers and the service process used in these heterogeneous data centers. Using queuing theory, we construct a complex queuing model composed of two concatenated queuing systems and present this as an analytical model for evaluating the performance of heterogeneous data centers. Based on this complex queuing model, we analyze the mean response time, the mean waiting time, and other important performance indicators. We also conduct simulation experiments to confirm the validity of the complex queuing model. We further conduct numerical experiments to demonstrate that the traffic intensity (or utilization) of each execution server, as well as the configuration of server clusters, in a heterogeneous data center will impact the performance of the system. Our results indicate that our analytical model is effective in accurately estimating the performance of the heterogeneous data center.

APA, Harvard, Vancouver, ISO, and other styles

30

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

31

Singh, Dilbag, Jaswinder Singh, and Amit Chhabra. "Failures in Cloud Computing Data Centers in 3-tier Cloud Architecture." International Journal of Information Engineering and Electronic Business 4, no. 3 (July 1, 2012): 1–8. http://dx.doi.org/10.5815/ijieeb.2012.03.01.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Hernandez, Lenonel, Genett Jimenez, and Piedad Marchena. "Energy Efficiency Metrics of University Data Centers." Knowledge Engineering and Data Science 1, no. 2 (August 23, 2018): 64. http://dx.doi.org/10.17977/um018v1i22018p64-73.

Full text

Abstract:

The data centers are fundamental pieces in the network and computing infrastructure, and evidently today more than ever they are relevant. Since they support the processing, analysis, assurance of the data generated in the network and by the applications in the cloud, which every day increases its volume thanks to technologies such as Internet of Things, Virtualization, and cloud computing, among others. Precisely the management of this large volume of information makes the data centers consume a lot of energy, generating great concern to owners and administrators. Green Data Centers offer a solution to this problem, reducing the impact produced by the data centers in the environment, through the monitoring and control of these. The metrics are the tools that allow us to measure in our case the energy efficiency of the data center and evaluate if it is friendly to the environment. These metrics will be applied to the data centers of the ITSA University Institution, Barranquilla and Soledad campus, and the analysis of these will be carried out. In previous research, the most common metric (PUE) was analyzed to measure the efficiency of the data centers, to verify if the University's data center is friendly to the environment. It is planned to extend this study by carrying out an analysis of several metrics to conclude which is the most efficient and which allows defining the guidelines to update or convert the data center in a friendly environment.

APA, Harvard, Vancouver, ISO, and other styles

33

Fang, Fang, and Xiao Feng Yu. "Building the Next-Generation Data Centers Infrastructure Cloud." Applied Mechanics and Materials 610 (August 2014): 601–5. http://dx.doi.org/10.4028/www.scientific.net/amm.610.601.

Full text

Abstract:

In order to adapt to the development trend of cloud computing, next-generation data center virtualized computing, storage and other resources to provide users with dynamic deployment of resources through the network, Equipment and technology such as servers, storage, security and software to connect together to form the infrastructure cloud platforms play a key supporting role. This paper focuses on network bandwidth, cabling systems, network protocols, and explores the next-generation data center infrastructure construction. The important idea is how to deploy more energy-efficient and low-cost, on-demand service-oriented infrastructure cloud.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

35

Ruckshana, K., and G. Ravi. "Network Traffic Analysis in Cloud: A Survey." Asian Journal of Computer Science and Technology 8, S2 (March 5, 2019): 61–65. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2023.

Full text

Abstract:

A data center (DC) denotes to any huge faithful group of computers that is retained and operated by an organization. Data centers of numerous sizes are being made and hired for a dissimilar set of resolves today. On the one hand, big universities and isolated enterprises gradually consolidating their IT services within on-site data centers comprising hundreds to thousands of servers. On the other hand, huge online service providers such as Google, Microsoft, and Amazon quickly constructing geographically varied cloud data centers often have more than 10K servers; to offer a variation of cloud-based services such as Email, Web servers, Gaming, Storage, and Instant Messaging. Though there is great interest in planning developed networks for data centers, very little is identified about the network-level traffic characteristics of present data centers. In this paper, we focused on a study of the network traffic in data centers and defining the anomaly detection system in secure cloud computing environment.

APA, Harvard, Vancouver, ISO, and other styles

36

Teli, Prasad, Manoj V. Thomas, and K. Chandrasekaran. "Big Data Migration between Data Centers in Online Cloud Environment." Procedia Technology 24 (2016): 1558–65. http://dx.doi.org/10.1016/j.protcy.2016.05.135.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Jose, G. Sahaya Stalin, and C. Seldev Christopher. "Error Correction Codes for Secure Cloud Data Centers." Asian Journal of Research in Social Sciences and Humanities 6, no. 12 (2016): 841. http://dx.doi.org/10.5958/2249-7315.2016.01333.2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Nan, Sambhunath. "Role of Solar Energy for Cloud Data Centers." Journal of the Association of Engineers, India 86, no. 3-4 (December 1, 2016): 78. http://dx.doi.org/10.22485/jaei/2016/v86/i3-4/130884.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

TAKANO, Ryousei, and Kuniyasu SUZAKI. "Disaggregated Accelerator Management System for Cloud Data Centers." IEICE Transactions on Information and Systems E104.D, no. 3 (March 1, 2021): 465–68. http://dx.doi.org/10.1587/transinf.2020edl8040.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Li, Yang Ping, Shao Fen Zhong, Xiao Heng Pan, and Hua Qiang Yuan. "Energy-Efficient Data Processing in Cloud Computing Centers." Advanced Materials Research 910 (March 2014): 397–400. http://dx.doi.org/10.4028/www.scientific.net/amr.910.397.

Full text

Abstract:

Rapid growth in the cloud computing centers worldwide is posing serious challenges to both hardware and software designers on the energy efficiency issues. This paper explores particular challenges and potential promises on the part of data processing in these centers.

APA, Harvard, Vancouver, ISO, and other styles

41

Chaudhry, Muhammad Tayyab, M. Hasan Jamal, Zeeshan Gillani, Waqas Anwar, and Muhammad Salman Khan. "Thermal-benchmarking for cloud hosting green data centers." Sustainable Computing: Informatics and Systems 25 (March 2020): 100357. http://dx.doi.org/10.1016/j.suscom.2019.100357.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Assi, Chadi, Sara Ayoubi, Samir Sebbah, and Khaled Shaban. "Towards Scalable Traffic Management in Cloud Data Centers." IEEE Transactions on Communications 62, no. 3 (March 2014): 1033–45. http://dx.doi.org/10.1109/tcomm.2014.012614.130747.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Luo, Liang, Wenjun Wu, W. T. Tsai, Dichen Di, and Fei Zhang. "Simulation of power consumption of cloud data centers." Simulation Modelling Practice and Theory 39 (December 2013): 152–71. http://dx.doi.org/10.1016/j.simpat.2013.08.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Gutierrez-Garcia, J. Octavio, and Adrian Ramirez-Nafarrate. "Agent-based load balancing in Cloud data centers." Cluster Computing 18, no. 3 (May 24, 2015): 1041–62. http://dx.doi.org/10.1007/s10586-015-0460-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Wu, H., A. N. Tantawi, Y. Diao, and W. Wang. "Adaptive memory load management in cloud data centers." IBM Journal of Research and Development 55, no. 6 (November 2011): 5:1–5:10. http://dx.doi.org/10.1147/jrd.2011.2170869.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Mishra, Mayank, and Umesh Bellur. "Unified resource management in cloud based data centers." CSI Transactions on ICT 5, no. 4 (April 17, 2017): 361–74. http://dx.doi.org/10.1007/s40012-017-0168-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Al-Ayyoub, Mahmoud, Muneera Al-Quraan, Yaser Jararweh, Elhadj Benkhelifa, and Salim Hariri. "Resilient service provisioning in cloud based data centers." Future Generation Computer Systems 86 (September 2018): 765–74. http://dx.doi.org/10.1016/j.future.2017.07.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Agarwal, Amit, and Ta Nguyen Binh Duong. "Secure virtual machine placement in cloud data centers." Future Generation Computer Systems 100 (November 2019): 210–22. http://dx.doi.org/10.1016/j.future.2019.05.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Bukhsh, Rasool, Nadeem Javaid, Zahoor Ali Khan, Farruh Ishmanov, Muhammad Afzal, and Zahid Wadud. "Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid." Energies 11, no. 12 (November 30, 2018): 3345. http://dx.doi.org/10.3390/en11123345.

Full text

Abstract:

The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud–fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.

APA, Harvard, Vancouver, ISO, and other styles

50

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!