• The Journal of Korean Association of Computer Education
• /
• v.13 no.3
• /
• pp.55-63
• /
• 2010

In order to solve the problems of maintenance and security for information-infrastructure, public organizations and some of leading companies adopted Server Based Computing(SBC) infrastructure. The effectiveness and possibility of SBC has become focused with the Cloud-Computing infrastructure, which is a extended concept of SBC, as it is being magnified as a main part among the internet business models for the next generation. The purpose of this study was to analyze its probability in elementary and secondary school and find out its effectiveness. In order to do this, three model schools have been selected from GyeongBuk, ChungNam, ChungBuk province and they were managed by SBC infrastructure. And We conducted analysis of satisfaction for teachers and students, interview with teachers and classroom observation as a effectiveness verification. As the results of the analysis, First, we can find out which part we should consider more when we are to adopt SBC infrastructure. Second, the level of satisfaction for teachers is 3.45 and students is 3.2. Therefore, this study was concluded to contribute to find directions what should be considered when setting the SBC infrastructure in elementary and secondary schools.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.9
• /
• pp.2113-2120
• /
• 2013

As a smart mobile device is gaining popularity as a tool to utilize internet services, the demand on the cloud service (which eliminates the spatial and/or temporal restrictions in enjoying the services) is increasing. This trend also has an effect on the style of performing business tasks so that the concept of 'smart-work' (e.g. doing business tasks in anywhere and anytime) is being spread over the industries. The facilities for the smart-work is, however, focused on providing the tools designed to supporting business tasks such as co-working in writing documents, video conference, searching data via smart (mobile) devices neglecting the industries' needs for the infrastructure by which they can create their own business applications and mobile cloud services at a low (or moderate) price. The paper proposes a mobile cloud service platform, which provides an SDK for developing business applications and offers the applications to smart mobile devices as a PaaS (Platform as a Service).

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.8
• /
• pp.1818-1824
• /
• 2015

Cloud computing draws attention as an application to provide dynamically scalable infrastructure for application, data and file storage. An untrusted remote server can cause a variety of problems in the field of data protection. It may process intentionally or involuntarily user's data operations(modify, insert, delete) without user's permission. It may provide false information in order to hide his mistakes in the auditing process. Therefore, it is necessary to audit the integrity of data stored in the cloud server. In this paper, we propose a new data auditing system that can verify whether servers had a malicious behavior or not. Performance and security analysis have proven that our scheme is suitable for cloud computing environments in terms of performance and security aspects.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.5
• /
• pp.27-35
• /
• 2014

The characteristics of today's health care industry, based on the state of the art IT can be represented as a paradigm of human-oriented ubiquitous and accessible as possible by U-Health care. In addition, the healthcare industry is information and communication technologies (ICT) developments regarding the many advances and applications based on the research being carried out actively. Medical information system has been developed toward combining information systems of medical IT and it sets its sights on the fusion of developed IT and u-healthcare system. So changing distributed medical information systems into a safe PHR integrated system based on IaaS cloud computing is suggested in order to forge u-healthcare system with the times in this paper. Our experimental results show that our proposed system increased the data access time by about 24% and reduces the waiting time for processing service by about 4.3% over the web-based PHR.

• The Journal of Society for e-Business Studies
• /
• v.19 no.1
• /
• pp.15-42
• /
• 2014

Cloud storage service has the potential to be a core infrastructure for the future mobile and Internet service; thus related service providers have been investing in it and trying to attract as many users as possible. In addition, those need to find out what motivates the users to keep using their service not only to attract new customers but also to secure their subscribers. Therefore, this study will examine its relationship with user's motivation based on the extended TAM model with external variables for objective research about continuous use of cloud storage service. As a result, it was found that personal innovativeness, self efficacy, functional attributes, and psychological switching cost influence the continuous use of cloud storage service. Also, it is expected they can guide service providers to the right track when setting up their business strategy in the future.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.2
• /
• pp.165-170
• /
• 2021

Machine learning has recently been applied to research in most areas. This is because the results of machine learning are not determined, but the learning of input data creates the objective function, which enables the determination of new data. In addition, the increase in accumulated data affects the accuracy of machine learning results. The data collected here is an important factor in machine learning. The proposed system is a convergence system of cloud systems and local fog systems for service delivery. Thus, the cloud system provides machine learning and infrastructure for services, while the fog system is located in the middle of the cloud and the user to collect and refine data. The data for this application shall be based on the Sensitive data generated by smart devices. The machine learning technique applied to this system uses SVM algorithm for classification and RNN algorithm for status recognition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.05a
• /
• pp.823-826
• /
• 2012

In this paper, we propose an efficient network virtualization model in cloud computing environments. Virtualization is a key technology for the implementation of service-oriented architecture. It is a standardized framework that can be reused or integrated with changing business priorities through a IT infrastructure. Network virtualization has emerged as an important technical issues of the future virtualization technology in Internet. The concept of network virtualization and related technologies stay in ambiguous status since network virtualization is in its early stage. Thus, we propose a network virtualization model for cloud environment by analyzing the existing network virtualization technologies.

• Journal of Convergence for Information Technology
• /
• v.10 no.10
• /
• pp.32-39
• /
• 2020

In the large-scale infrastructure of cloud environment, illegal access rights are frequently caused by sharing applications and devices, so in order to actively respond to such attacks, a strengthened access control system is required to prepare for each situation. We proposed an entity attribute-based access control(EABAC) model based on security level and relation concept. This model has enhanced access control characteristics that give integrity and confidentiality to subjects and objects, and can provide different services to the same role. It has flexibility in authority management by assigning roles and rights to contexts, which are relations and context related to services. In addition, we have shown application cases of this model in multi service environment such as university.

• ETRI Journal
• /
• v.42 no.5
• /
• pp.658-668
• /
• 2020

In edge computing, most procedures, including data collection, data processing, and service provision, are handled at edge nodes and not in the central cloud. This decreases the processing burden on the central cloud, enabling fast responses to end-device service requests in addition to reducing bandwidth consumption. However, edge nodes have restricted computing, storage, and energy resources to support computation-intensive tasks such as processing deep neural network (DNN) inference. In this study, we analyze the effect of models with single and multiple local exits on DNN inference in an edge-computing environment. Our test results show that a single-exit model performs better with respect to the number of local exited samples, inference accuracy, and inference latency than a multi-exit model at all exit points. These results signify that higher accuracy can be achieved with less computation when a single-exit model is adopted. In edge computing infrastructure, it is therefore more efficient to adopt a DNN model with only one or a few exit points to provide a fast and reliable inference service.

• Journal of Information Processing Systems
• /
• v.8 no.4
• /
• pp.555-566
• /
• 2012

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.