• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2013년도 춘계학술발표대회
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• pp.507-510
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• 2013

Cloud computing is an emerging technology, which allows the user to fulfill his needs by outsourcing the resources. With the passage of time, cloud computing has become an essential part of our lives. But it still requires some sort of standardization, specially in terms of user's trust, privacy, and security related things. This study presents different types of cloud computing services and their working domains along with some key virtualization related issues that are encountered by the cloud service provider as well as the user. Those key issues, related with virtual network are discussed in this paper. This study provides a basis to work further on those issues, so that the key concerns are addressed as soon as possible and cloud computing could become standardized and more prevalent.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.158-170
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• 2021

Virtualization technologies are being adopted and broadly utilized in many fields and at different levels. In cloud computing, achieving load balancing across large distributed virtual machines is considered a complex optimization problem with an essential importance in cloud computing systems and data centers as the overloading or underloading of tasks on VMs may cause multiple issues in the cloud system like longer execution time, machine failure, high power consumption, etc. Therefore, load balancing mechanism is an important aspect in cloud computing that assist in overcoming different performance issues. In this research, we propose a new approach that combines the advantages of different task allocation algorithms like Round robin algorithm, and Random allocation with different threshold techniques like the VM utilization and the number of allocation counts using least connection mechanism. We performed extensive simulations and experiments that augment different scheduling policies to overcome the resource utilization problem without compromising other performance measures like makespan and execution time of the tasks. The proposed system provided better results compared to the original round robin as it takes into consideration the dynamic state of the system.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.163-172
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• 2022

The interconnection of an enormous number of devices into the Internet at a massive scale is a consequence of the Internet of Things (IoT). As a result, tasks offloading from these IoT devices to remote cloud data centers become expensive and inefficient as their number and amount of its emitted data increase exponentially. It is also a challenge to optimize IoT device energy consumption while meeting its application time deadline and data delivery constraints. Consequently, Fog Computing was proposed to support efficient IoT tasks processing as it has a feature of lower service delay, being adjacent to IoT nodes. However, cloud task offloading is still performed frequently as Fog computing has less resources compared to remote cloud. Thus, optimized schemes are required to correctly characterize and distribute IoT devices tasks offloading in a hybrid IoT, Fog, and cloud paradigm. In this paper, we present a detailed survey and classification of of recently published research articles that address the energy efficiency of task offloading schemes in IoT-Fog-Cloud paradigm. Moreover, we also developed a taxonomy for the classification of these schemes and provided a comparative study of different schemes: by identifying achieved advantage and disadvantage of each scheme, as well its related drawbacks and limitations. Moreover, we also state open research issues in the development of energy efficient, scalable, optimized task offloading schemes for Fog computing.

• Asia pacific journal of information systems
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• 제27권4호
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• pp.191-215
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• 2017

This study investigated the antecedents and the performance impact of two types of Smartphone functions (communication vs. computing functions) in organizational environment and the moderating impact of Smartphone users' organizational roles. More specifically, identifying two distinct types of Smartphone functions such as communication functions and computing functions (including informational, social network, and resource management functions), we investigated the impact of three antecedents (Smartphone dependency, task mobility, and perceived critical mass) on the use of the two Smartphone functions and how organizational workers' perceived performance gains differ by using these two different Smartphone functions for their workplace activities. We tested our hypotheses with survey data collected from 176 organizational workers. Our findings suggest that Smartphone dependency, task mobility and perceived critical mass of Smartphone use are significantly associated with the use of the two different functions, and that the use of computing functions is more strongly associated with perceived performance gain than the use of communication functions. We also found that managerial roles played by individual workers differently moderate the impact of Smartphone use on perceived performance gain. The present findings enable researchers and practitioners to better understand the impact of Smartphone use in workplaces.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1163-1185
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• 2024

With the sharp increase in the volume of literature data, researchers must spend considerable time and energy locating desired papers. A paper recommendation is the means necessary to solve this problem. Unfortunately, the large amount of data combined with sparsity makes personalizing papers challenging. Traditional matrix decomposition models have cold-start issues. Most overlook the importance of information and fail to consider the introduction of noise when using side information, resulting in unsatisfactory recommendations. This study proposes a paper recommendation method (PR-SLSMF) using document-level representation learning with citation-informed transformers (SPECTER) and low-rank and sparse matrix factorization; it uses SPECTER to learn paper content representation. The model calculates the similarity between papers and constructs a weighted heterogeneous information network (HIN), including citation and content similarity information. This method combines the LSMF method with HIN, effectively alleviating data sparsity and cold-start issues and avoiding topic drift. We validated the effectiveness of this method on two real datasets and the necessity of adding side information.

• International journal of advanced smart convergence
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• 제9권1호
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• pp.109-112
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• 2020

Many devices are connected on the internet to give functionalities for interconnected services. In 2020', The number of devices connected to the internet will be reached 5.8 billion. Moreover, many connected service provider such as Google and Amazon, suggests edge computing and mesh networks to cope with this situation which the many devices completely connected on their networks. This paper introduces the current state of the introduction of the wireless mesh network and edge cloud in order to efficiently manage a large number of nodes in the exploding Internet of Things (IoT) network and introduces the existing Network Time Protocol (NTP). On the basis of this, we propose a relatively accurate time synchronization method, especially in heterogeneous mesh networks. Using this NTP, multiple time coordinators can be placed in a mesh network to find the delay error using the average delay time and the delay time of the time coordinator. Therefore, accurate time can be synchronized when implementing IoT, remote metering, and real-time media streaming using IoT mesh network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.484-503
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• 2016

Mobile cloud computing has recently become a new paradigm for the utilization of a variety of shared mobile resources via wireless network environments. However, due to the inherent characteristics of mobile devices, a limited battery life, and a network access requirement, it is necessary for mobile servers to provide a dynamic approach for managing mobile resources efficiently in mobile cloud computing environments. Since on-demand job requests occur frequently and the number of mobile devices is drastically increased in mobile cloud computing environments, a different mobile resource management method is required to maximize the computational power. In this paper, we therefore propose a cooperative, mobile resource sharing method that considers both the inherent properties and the number of mobile devices in mobile cloud environments. The proposed method is composed of four main components: mobile resource monitor, job handler, resource handler, and results consolidator. In contrast with conventional mobile cloud computing, each mobile device under the proposed method can be either a service consumer or a service provider in the cloud. Even though each device is resource-poor when a job is processed independently, the computational power is dramatically increased under the proposed method, as the devices cooperate simultaneously for a job. Therefore, the mobile computing power throughput is dynamically increased, while the computation time for a given job is reduced. We conduct case-based experiments to validate the proposed method, whereby the feasibility of the method for the purpose of cooperative computation is shown.

• 정보보호학회논문지
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• 제21권6호
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• pp.55-65
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• 2011

최근 유무선 통신 네트워크의 확산 및 고속화에 따라 인터넷 기술을 활용한 높은 수준의 확장성을 제공하는 클라우드 컴퓨팅 서비스(Cloud Computing Service) 이용이 증가하고 있다. 클라우드 컴퓨팅 서비스란 네트워크, 서버, 스토리지, 응용프로그램 등 다양한 컴퓨팅 자원들의 공유된 풀에 네트워크로 접근하여 언제든지 편리하게 사용 가능한 컴퓨팅 방식으로써 컴퓨팅 환경의 가상화라는 클라우드 컴퓨팅 서비스의 본질적인 특성으로 인해 디지털 포렌식 관점에서 사건 수사 시 데이터를 확보하는 일 자체가 어려운 현실에 직면했다. 본 논문에서는 클라우드 컴퓨팅 서비스에 대한 디지털 포렌식 관점의 연구와 IaaS 형태의 클라우드 컴퓨팅서비스 중 시장 점유율의 대부분을 차지하고 있는 AWS(Amazon Web Service)와 Rackspace에 대한 증거데이터 수집 및 분석방안을 제시한다.

• Communications for Statistical Applications and Methods
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• 제11권2호
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• pp.287-295
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• 2004

Mark Weiser introduced ubiquitous computing in his article titled 'The computer for 21st Century' in 1991. This has been new paradigm after internet. Now, the rapid development of mobile computer, wireless network, and intelligent system has supported ubiquitous computing environment. In the related area of information science, the researchers have studied on ubiquitous computing. But in the field of Korea statistics, this research has not been worked yet. So, we proposed the connection between statistics and ubiquitous computing in this paper. As an example, we showed an efficient cache hoarding for ubiquitous computing using statistical methods. In experimental results, we verified our proposed issue.