• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.99-105
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• 2012

Given the numbers of smartphones, tablets and other mobile devices shipped every day, more and more users are relying on the cloud as the main driver for satisfying their computing needs, whether it is data storage, applications or infrastructure. Mobile cloud computing is simply cloud computing in which at least some of the devices involved are mobile. Each node is owned by a different user and is likely to be mobile. Using mobile hardware for cloud computing has advantages over using traditional hardware. These advantage include computational access to multimedia and sensor data without the need for large network transfer, more efficient access to data stored on other mobile devices and distributed ownership and maintenance of hardware. It is important to predict job execution time in mobile cloud computing because there are many mobile nodes with different capabilities. This paper analyzes the job execution time for mobile cloud computing in terms of network environment and heterogeneous mobile nodes using a mathematical model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.1998-2014
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• 2016

In recent years, cloud computing services based on smart phones and other mobile terminals have been a rapid development. Cloud computing has the advantages of mass storage capacity and high-speed computing power, and it can meet the needs of different types of users, and under the background, mobile cloud computing (MCC) is now booming. In this paper, we have put forward a new classification-based virtual machine placement (CBVMP) algorithm for MCC, and it aims at improving the efficiency of virtual machine (VM) allocation and the disequilibrium utilization of underlying physical resources in large cloud data center. By simulation experiments based on CloudSim cloud platform, the experimental results show that the new algorithm can improve the efficiency of the VM placement and the utilization rate of underlying physical resources.

• Journal of The Korean Astronomical Society
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• v.27 no.2
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• pp.159-175
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• 1994

We have mapped the whole extent of a dark cloud Lynds 1251 in the emission of the J=1-0 transitions of $^{12}CO\;and\;^{13}CO$ using FCRAO's fifteen-beam array receiver in high angular resolution of 50'. We have derived physical parameters of L1251, discussed three different mass estimate techniques, and obtained a large range of mass, 600 to $6,000M_\bigodot$, depending on the techniques. The factor of 10 discrepancy between the virial and LTE masses is much larger than expected based on the uncertainties residing in two methods. The large virial mass may reflect the fact that L1251 is not gravitationally bound system as in the case of dark clouds in solar neighborhood. Two outflows are affecting the dynamics of cloud significantly but not enough to reshape the whole extent of the cloud. The small cloud, 'Stripe', which is apparently connected with main cloud, is not likely to be associated with L1251. The velocity gradient composed on this small cloud may be driven by other unknown sources. It is found that L1251 cloud itself is very quiescent except the two bipolar outflow regions.

• Journal of Integrative Natural Science
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• v.6 no.1
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• pp.12-15
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• 2013

To combine the micro rain radar and microwave radiometer cloud liquid water, we estimate the cloud physical thickness from the difference between the MTSAT-1R cloud top height and cloud base height of visual observation of Daegwallyeong weather station, and the cloud liquid water path of micro rain radar is obtained by multiplying the liquid water content of micro rain radar and the estimated cloud physical thickness. The trend of microwave radiometer liquid water path agrees with that of the micro rain radar during small precipitation. We study these characteristics of micro rain radar and microwave radiometer for small precipitation to obtain the combined cloud water content of micro rain radar and microwave radiometer, constantly operated regardless to the rainfall.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.132-144
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• 2014

Information always comes with security and risk problems. There is the saying that, "The tall tree catches much wind," and the risks from cloud services will absolutely be more varied and more severe. Nowadays, handling these risks is no longer just a technology problem. So far, a good deal of literature that focuses on risk or security management and frameworks in information systems has already been submitted. This paper analyzes the causal risk factors in cloud environments through critical success factors, from a business perspective. We then integrated these critical success factors into a business model for information security by mapping out 10 principles related to cloud risks. Thus, we were able to figure out which aspects should be given more consideration in the actual transactions of cloud services, and were able to make a business-level and general-risk control model for cloud computing.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.81-87
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• 2018

Cloud computing is a computing environment in which users borrow as many IT resources as they need to, and use them over the network at any point in time. This is the concept of leasing and using as many IT resources as needed to lower IT resource usage costs and increase efficiency. Recently, cloud computing is emerging to provide stable service and volume of data along with major technological developments such as the Internet of Things, artificial intelligence and big data. However, for a more secure cloud environment, the importance of perimeter security such as shared resources and resulting secure data storage and access control is growing. This paper analyzes security threats in cloud computing environments and proposes a security architecture for effective response.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.39-44
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• 2021

Since the recent Corona 19, the importance of cloud computing is increasing, and at the same time, competition among clouds is intensifying. Cloud companies are competing for survival by promoting various management innovation methods for continuous growth and development amid a rapidly changing business environment. They are also increasingly interested in performance management in their operations and growth. In this paper, we propose Cloud BSC, an IT BSC-based performance measurement model for cloud enterprise performance management. The validity of the proposed model is verified through statistical analysis and causal analysis. Eventually, the proposed model is expected to be utilized as a management evaluation tool that can provide useful performance analysis information to cloud companies.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.290-296
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• 2022

Big Data is a data analysis technology empowered by late advances in innovations and engineering. In any case, big data involves a colossal responsibility of equipment and handling assets, making reception expenses of big data innovation restrictive to little and medium estimated organizations. Cloud computing offers the guarantee of big data execution to little and medium measured organizations. Big Data preparing is performed through a programming worldview known as MapReduce. Normally, execution of the MapReduce worldview requires organized joined stockpiling and equal preparing. The computing needs of MapReduce writing computer programs are frequently past what little and medium measured business can submit. Cloud computing is on-request network admittance to computing assets, given by an external element. Normal arrangement models for cloud computing incorporate platform as a service (PaaS), software as a service (SaaS), framework as a service (IaaS), and equipment as a service (HaaS).

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.83-90
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• 2022

Traditional Cloud Computing would be unable to safely host IoT data due to its high latency as the number of IoT sensors and physical devices accommodated on the Internet grows by the day. Because of the difficulty of processing all IoT large data on Cloud facilities, there hasn't been enough research done on automating the security of all components in the IoT-Cloud ecosystem that deal with big data and real-time jobs. It's difficult, for example, to build an automatic, secure data transfer from the IoT layer to the cloud layer, which incorporates a large number of scattered devices. Addressing this issue this article presents an intelligent algorithm that deals with enhancing security aspects in IoT cloud ecosystem using butterfly optimization algorithm.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.65-72
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• 2023

Cloud Computing is one of the current research areas in computer science. Recently, Cloud is the buzz word used everywhere in IT industries; It introduced the notion of 'pay as you use' and revolutionized developments in IT. The rapid growth of modernized cloud computing leads to 24×7 accessing of e-resources from anywhere at any time. It offers storage as a service where users' data can be stored on a cloud which is managed by a third party who is called Cloud Service Provider (CSP). Since users' data are managed by a third party, it must be encrypted ensuring confidentiality and privacy of the data. There are different types of cryptographic algorithms used for cloud security; in this article, the algorithms and their security measures are discussed.