• Journal of Information Technology Services
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• v.17 no.3
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• pp.37-51
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• 2018

Recently, cloud computing market is growing geometrically in both private and public area, and many global companies in various domains have developed and provided cloud computing services. In such situation, Korean government made multiple plans for domestic cloud computing industry. However, most research institutes have focused on market size and status information, which makes actual effectiveness of cloud computing service hard to recognize. In this study, we define cloud computing Industry by rearranging Input-Output table published by the Bank of Korea to use Input-Output Analysis. The Input-Output Analysis was devised in 1963 by Leontief and it is used in many fields of study until now. It produces various coefficients that are able to identify production-inducing effect, value-added effect, labor-inducing effect, front and rear chain effect. The analysis results show that production-inducing effect, front and rear chain effect of cloud computing industry is low compared to other industries. However, cloud computing Industry possesses relatively high value-added effect and labor-inducing effect. It is because industry magnitude of cloud computing is smaller than other industries such as manufacturing, chemical industries. The economic effects of the cloud computing industry are not remarkable, but this result is significant to emerging markets and industries and presents the fresh way of analyzing cloud computing research field.

• Journal of Digital Contents Society
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• v.14 no.3
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• pp.313-321
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• 2013

In recent, proliferation of mobile smart devices have led to big-data era, the importance of location-based services is increasing due to the exponential growth of trajectory related data. In order to process trajectory data, parallel processing platforms such as cloud computing and MapReduce are necessary. Currently, the researches based on MapReduce are on progress, but due to the MapReduce's properties in using batch processing and simple key-value structure, applying MapReduce framework for real time LBS is difficult. Therefore, in this research we propose a suitable system design on efficient indexing and search techniques for real time service based on detailed analysis on the properties of MapReduce.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.173-196
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• 2012

Cloud computing is provided on demand service via the internet, allowing users to pay for the service they actually use. Since cloud computing is emerging stage in industry, many companies and government consider adopting the cloud computing. Actually a variety of factors may influence on the adopting decision making of cloud computing. The objective of this study is to explore the significant factors affecting the adoption decision of enterprise cloud computing. A research model has been suggested based on TOE framework and outsourcing decision framework. Based on 302 data collected from managers in various industries, the major findings are following. First, the benefit factors of cloud computing service such as agility and cost reduction have direct and positive effects on adoption of the service. Second, lock-in as a risk factor of cloud computing service has a negative effect while security has not. Third, both internal and external environment factors have positive effects on adoption of the service.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.155-171
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• 2012

With the continuous and outstanding development of information technology(IT), human being is coming to the new computing era which is called cloud computing. This era brings lots of huge benefits also at the same time release the resources of IT infrastructure and data boom for man. In the future no longer, most of IT service providers, enterprises, organizations and systems will adopt this new computing model. There are three main deployment models in cloud computing including public cloud, private cloud and hybrid cloud; each one also has its own cons and pros. While implementing any kind of cloud services, customers have to choose one of three above deployment models. Thus, our paper aims to represent a practical framework to help the adopter select which one will be the best suitable deployment model for their requirements by evaluating each model comprehensively. The framework is built by applying the analytic hierarchy process(AHP), namely benefit-cost-opportunity-risk(BCOR) model as a powerful and effective tool to serve the problem. The gained results hope not only to provide useful information for the readers but also to contribute valuable knowledge to this new area. In addition, it might support the practitioners' effective decision making process in case they meet the same issue and have a positive influence on the increase of right decision for the organization.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.497-500
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• 2018

데이터 센터를 기반으로 서비스를 수행하는 기업들은 비용절감을 위해 서버 가상화 기술을 이용한다. 서버 가상화를 이용하는 기업들은 대부분 하이퍼바이저 기반의 서버 가상화 기술을 사용하며, 이 경우 하드웨어 가상화를 통해 커널 단에서 많은 I/O와 리소스를 처리해야 한다. 따라서 하이퍼바이저 기반의 서비스는 느리다는 단점이 있으며 이를 해결하기 위해 컨테이너 기반의 가상화 기술을 이용할 수 있다. 하지만 컨테이너 기반의 네트워크 또한 문제점이 존재한다. 컨테이너 기반의 네트워크는 유연한 네트워크를 구성하기 어렵고, 기존의 컨테이너 네트워크 인터페이스를 활용할 경우 데이터 전송 성능이 저하된다. 본 논문에서는 컨테이너 오케스트레이션 툴인 Kubernetes와 SDN (Software-Defined Network) 기반의 가상전용 네트워크 연계 환경을 구축하고 이에 적합한 컨테이너 네트워크를 연구하여 이의 문제점을 해결한다. 즉, 가상전용 네트워크와 Kubernetes의 연계를 통해 고성능의 유연한 네트워크를 구성할 수 있는 프레임워크를 개발하여 기존 컨테이너 기반 네트워크와 비교하고 성능을 검증했다.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.137-148
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• 2012

Cloud service users request dedicated virtual computing resource from the cloud service provider to process jobs in independent environment from other users. To optimize this process with automated method, in this paper we proposed a framework for workflow scheduling in the cloud environment, in which the core component is the middleware called broker mediating the interaction between users and cloud service providers. To process jobs in on-demand and virtualized resources from cloud service providers, many papers propose scheduling algorithms that allocate jobs to virtual machines which are dedicated to one machine one job. With this method, the isolation of being processed jobs is guaranteed, but we can't use each resource to its fullest computing capacity with high efficiency in resource utilization. This paper therefore proposed a cost-efficient job scheduling algorithm which maximizes the utilization of managed resources with increasing the degree of multiprogramming to reduce the number of needed virtual machines; consequently we can save the cost for processing requests. We also consider the performance degradation in proposed scheme with thrashing and context switching. By evaluating the experimental results, we have shown that the proposed scheme has better cost-performance feature compared to an existing scheme.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.1-12
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• 2015

To realize futuristic services with agility, the role of the experimental facility (i.e., testbed) based on integrated resources has become important, so that developers can flexibly utilize the dynamic provisioning power of software-defined networking and cloud computing. Following this trend, an OpenFlow-based SDN testbed environment, denoted as OF@TEIN, connects multiple sites with unique SmartX Racks (i.e., virtualization-enabled converged resources). In this paper, in order to automate the multi-point L2 (i.e., Ethernet) inter-connection of OpenFlow islands, we introduce an automated tool to configure the required Network Virtualization using Generic Routing Encapsulation (NVGRE) tunneling. With the proposed automation tool, the operators can efficiently and quickly manage network inter-connections among multiple OpenFlow sites, while letting developers to control their own traffic flows for service realization experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.61-69
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• 2015

Through Virtual Machine technology(VM), VMs can be packed into much fewer number of physical servers than that of VMs. Since even an idle physical server wastes more than 60% of max power consumption, it has been considered as one of energy saving technologies to minimize the number of physical servers by using the knapsack problem solution based on the computing resources. However, this paper shows that this tightly packed consolidation may not achieve the efficient energy saving. Instead, a service pattern-based VM consolidation algorithm is proposed. The proposed algorithm takes the service time of each VM into account, and consolidates VMs to physical servers in the way to minimize energy consumption. The comprehensive simulation results show that the proposed algorithm gains more than 30% power saving.

• Journal of Platform Technology
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• v.11 no.3
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• pp.3-11
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• 2023

Real-time control systems are growing rapidly as infrastructure technologies such as IoT and mobile communication develop and services that value real-time such as factory management and vehicle operation checks increase. Various solutions have been proposed to increase the time sensitivity of this system, but most real-time control systems are currently composed of local servers and multiple clients located in control stations, which are transmitted to local servers where control systems are located. In this paper, we proposed an edge computing-based real-time control model that can reduce the time it takes for the bus information system, one of the real-time control systems, to provide the information to the user at the time it collects the information. Simulating the existing model and the edge computing model, the edge computing model confirmed that the cost for users to receive data is reduced from at least 10% to up to 80% compared to the existing model.

• Research in Plant Disease
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• v.30 no.3
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• pp.256-267
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• 2024

We have developed FBcastS (Fire Blight Forecasting System), a cloud-based information system that leverages the K-Maryblyt forecasting model. The FBcastS provides an optimal timing for spraying antibiotics to prevent flower infection caused by Erwinia amylovora and forecasts the onset of disease symptoms to assist in scheduling field scouting activities. FBcastS comprises four discrete subsystems tailored to specific functionalities: meteorological data acquisition and processing, execution of the K-Maryblyt model, distribution of web-based information, and dissemination of spray timing notifications. The meteorological data acquisition subsystem gathers both observed and forecasted weather data from 1,583 sites across South Korea, including 761 apple or pear orchards where automated weather stations are installed for fire blight forecast. This subsystem also performs post-processing tasks such as quality control and data conversion. The model execution subsystem operates the K-Maryblyt model and stores its results in a database. The web-based service subsystem offers an array of internet-based services, including weather monitoring, mobile services for forecasting fire blight infection and symptoms, and nationwide fire blight monitoring. The final subsystem issues timely notifications of fire blight spray timing alert to growers based on forecasts from the K-Maryblyt model, blossom status, pesticide types, and field conditions, following guidelines set by the Rural Development Administration. FBcastS epitomizes a smart agriculture internet of things (IoT) by utilizing densely collected data with a spatial resolution of approximately 4.25 km to improve the accuracy of fire blight forecasts. The system's internet-based services ensure high accessibility and utility, making it a vital tool in data-driven smart agricultural practices.