• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.235-240
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• 2013

Cloud systems are popular computing environment because they can provide easy access to computing resources for users as well as efficient use of resources for companies. The resources of cloud computing are heterogeneous and jobs have various characteristics. One such issue is effective job scheduling. Scheduling in the cloud system may be defined as a multiple criteria decision model. To address this issue, this paper proposes a priority-based two-step job scheduling algorithm. On the first level, jobs are classified based on preference. Resources are dedicated to a job if a deadline failure would cause severe results or critical business losses. In case of only minor discomfort or slight functional impairment, the job is scheduled using a best effort approach. On the second level, jobs are allocated to adequate resources through their priorities that are calculated by the analytic hierarchic process model. We then analyze the proposed algorithm and make a scheduling example to confirm its efficiency.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.9-16
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• 2017

In this paper, we propose to present a model of cloud security that has emerged as the biggest topic of cloud computing, replacing the traditional IT environment. While cloud computing is an extension of existing IT technology, security issues and threats can be applied to traditional security technologies. However, the biggest difference between a typical computing environment and a cloud computing environment is a virtualized environment with a hypervisor. Currently, there are many weaknesses in the virtualized environment, and there are few related security products. In order for a cloud computing environment to function as a reliable IT environment, we expect more research on hypervisor-based security technologies, and we expect to secure safer cloud services through a secure model over the next generation of new-based networks.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.89-92
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• 2018

본 논문에서는 Edge computing 환경에서 다수의 노드들로 구성된 네트워크의 디바이스를 효율적으로 관리하기 위한 방법을 제안한다. 기존의 클라이언트-서버 모델은 모든 데이터와 그에 대한 요청을 중심 서버에서 처리하기 때문에, 다수의 노드로부터 생성된 많은 양의 데이터를 처리하는 데 빠른 응답속도를 보장하지 못한다. Edge computing은 분담을 통해 네트워크의 부담을 줄일 수 있는 IoT 네트워크에 적합한 방법으로, 데이터를 전송하고 받는 과정에서 네트워크의 대역폭을 사용하는 대신 서로 연결된 노드들이 협력해서 데이터를 처리하고, 또한 네트워크 말단에서의 데이터 처리가 허용되어 데이터 센터의 부담을 줄일 수 있다. 여러병렬 기계학습 모델 중 본 연구에서는 Stale Synchronous Parallel(SSP) 모델을 이용하여 Edge 노드에서 분산기계 학습에 적용하였다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.51-57
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• 2014

This paper presents an algorithmic type computing technique of process coefficient in predicting model of temperature for reheating furnace and also suggests a design method of neural network model to find an adequate value of process coefficient for arbitrary operating conditions including test conditons. The proposed neural network use furnace temperature, line speed and slab information as input variables, and process coefficient is output variable. Reasonable process coefficients can be obtained by an algorithmic procedure proposed in this paper using process data gathered at test conditons. Also, neural network model output equal process coefficient under same input conditions. This means that adquate process coefficients can be found by only computing neural network model without additive test even if operating conditions vary.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.141-148
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• 2023

As the direction of education in the fourth industry in the 21st century, convergence talent education that emphasizes the connection and convergence between core competency-based education and academia is emerging to foster creative talent. The purpose of this paper is to present the criteria for evaluating the competency of learning outcomes in order to develop a strategic model for innovation in engineering teaching-learning. In this paper, as a study to establish the direction of implementation of convergence talent education, a creative innovation teaching method support system was established to improve the quality of convergence talent education. Firstly, a plan to develop a teaching-learning model based on computing thinking. Secondly, it presented the development of a teaching-learning model based on linkage and convergence learning. Thirdly, we would like to present educational appropriateness and ease based on convergence learning in connection with curriculum improvement strategies based on computing thinking skills. Finally, we would like to present a strategic model development plan for innovation in engineering teaching-learning that applies the convergence talent education program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4087-4107
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• 2016

The current cloud computing paradigm is still vulnerable to a significant number of system failures. The increasing demand for fault tolerance and resilience in a cost-effective and device-independent manner is a primary reason for creating an effective means to address system dependability and availability concerns. This paper focuses on online failure prediction for cloud computing systems using system runtime data, which is different from traditional tolerance techniques that require an in-depth knowledge of underlying mechanisms. A 'failure prediction' approach, based on Cloud Theory (CT) and the Hidden Markov Model (HMM), is proposed that extends the HMM by training with CT. In the approach, the parameter ω is defined as the correlations between various indices and failures, taking into account multiple runtime indices in cloud computing systems. Furthermore, the approach uses multiple dimensions to describe failure prediction in detail by extending parameters of the HMM. The likelihood and membership degree computing algorithms in the CT are used, instead of traditional algorithms in HMM, to reduce computing overhead in the model training phase. Finally, the results from simulations show that the proposed approach provides very accurate results at low computational cost. It can obtain an optimal tradeoff between 'failure prediction' performance and computing overhead.

• Journal of Korea Technology Innovation Society
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• v.18 no.1
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• pp.176-203
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• 2015

While the importance of adopting cloud computing service has been emerged, comparatively little research has been conducted on examining factors of an individual user's intention to switch toward cloud computing service. Hereafter, this study presents and empirically tests users' intention to switch to cloud computing. Our model posits that the characteristics of cloud computing such as effectiveness, economics, accessability, switching cost, security concern, and satisfaction toward existing IT service to cloud service affect perceived value, which in turn, influences intention to switch. An experimental study using student subjects provided empirical validation for our proposed model. Survey data from 204 respondents was used to test the model using partial least square analysis. As the result of the analysis, five hypotheses out of seven hypotheses were supported. According to our results, among the characteristics of cloud computing, effectiveness, economics, switching cost, and security concern were found to have significant impact on users' intention to switch that mediated by perceived value. Based on our research findings, we hope that this research will stimulate researchers' interest in the emerging area of cloud computing adoption.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.27-32
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• 2018

IoT technology is developing with various application areas in $4^{th}$ Industrial revolution. There are many users using the application services. Sensing data from various environment need to be transferred to cloud computing storage and store in the cloud storage. However, physical distance from the end node to cloud computing storage is far away, and it is not efficient to transfer data from sensors and store the sensing data in the cloud storage whenever sensing data happen. Therefore, Fog computing is proposed to solve these problems which can process and store the sensing data. However, Fog computing is new emerging technology, there is no standard security model and requirements. This research proposes to security requirements and security model for Fog computing to establish a secure and efficient cloud computing environment.

• Journal of the Korea Society of Computer and Information
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• v.22 no.8
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• pp.55-61
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• 2017

Cloud computing is cost-effective in terms of system configuration and maintenance and does not require special IT skills for management. Also, cloud computing provides an access control setting where SSO is adopted to secure user convenience and availability. As the SSO user authentication structure of cloud computing is exposed to quite a few external security threats in wire/wireless network integrated service environment, researchers explore technologies drawing on distributed SSO agents. Yet, although the cloud computing access control using the distributed SSO agents enhances security, it impacts on the availability of services. That is, if any single agent responsible for providing the authentication information fails to offer normal services, the cloud computing services become unavailable. To rectify the environment compromising the availability of cloud computing services, and to protect resources, the current paper proposes a security policy that controls the authority to access the resources for cloud computing services by applying the authentication policy of user authentication agents. The proposed system with its policy of the authority to access the resources ensures seamless and secure cloud computing services for users.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.101-107
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• 2024

Cloud computing is a widely used technology that has changed the way people and organizations store and access information. This technology is quite versatile, which is why extensive amounts of data can be stored in the cloud. Furthermore, businesses can access various services over the cloud without having to install applications. However, the cloud computing services are provided over a public domain, which means that both trusted and non-trusted users can access the services. Though there are several advantages of cloud computing services, especially to business owners, various challenges are also posed in terms of the privacy and security of information and online services. A kind of threat that is widely faced in the cloud environment is the on/off attack. In this kind of attack, a few entities exhibit proper behavior for a given time period to develop a highly a positive reputation and gather trust, after which they exhibit deception. A viable solution is provided by the given trust model for preventing the attacks. This method works by providing effective security to the cloud services by identifying malicious and inappropriate behaviors through the application of trust algorithms that can identify on-off attacks.