• Asia pacific journal of information systems
• /
• 제24권4호
• /
• pp.473-491
• /
• 2014

There is a growing interest for 'Cloud computing' to cut costs, and help the users focus on their core business instead of being impeded by IT obstacles. As it became IT version 3.0 which represents the era of cloud services and the dominance of a new kind of IT service provider, cloud service providers (CSPs)' role is more critical as a trusted IT advisor to include cloud migration and integration expertise. However, previous literatures related to cloud computing service have mainly analyzed from customers, although it is hard for customers to totally understand the complex and diverse cloud ecosystem. Therefore, it is an urgent task to mitigate the inhibitory factors in providing the cloud services for activating cloud industry. This study, an exploratory research based on interviews, has derived factors of promoting and hindering the activation of cloud computing from the provider's perspective, and has analyzed a sequence of cause and effect for each factor. For this, the casual loop diagram was developed to deduce key issues, and propose an alternative. The results of this study are expected to help activate 'Cloud computing' in Korea by minimizing the potential negative effects of activating 'Cloud computing' industry.

• 디지털융복합연구
• /
• 제7권4호
• /
• pp.43-56
• /
• 2009

The concept causing all the fuss is "the utility computing or the usage-based IT service", which now represents the future for IT asset in all aspects of the way they work in business, the commercial and public sector. The core of "utility computing or usage-based IT service" is changing the IT assert from "ownership" to "borrowing", which enables managers to get greater utilization of data-centre resources at lower operating costs. This trend is spreaded in public sector centering the Governmental Internet data Center of Korea(NCIA). So, it has need to make an usage-based IT service model that is suitable for public sector. In this paper, we propose the usage-based IT service model that is composed of IT service framework, service pricing model and IT service architecture.

• IEIE Transactions on Smart Processing and Computing
• /
• 제6권3호
• /
• pp.158-167
• /
• 2017

Internet-based social and interactive video applications have become major constituents of the envisaged applications for next-generation multimedia networks. However, inherently dynamic network conditions, together with varying user expectations, pose many challenges for resource allocation mechanisms for such applications. Yet, in addition to addressing these challenges, service providers must also consider how to mitigate their operational costs (e.g., energy costs, equipment costs) while satisfying the end-user quality of service (QoS) expectations. This paper proposes a heuristic solution to the problem, where the energy incurred by the applications, and the monetary costs associated with the service infrastructure, are minimized while simultaneously maximizing the average end-user QoS. We evaluate the performance of the proposed solution in terms of serving probability, i.e., the likelihood of being able to allocate resources to groups of users, the computation time of the resource allocation process, and the adaptability and sensitivity to dynamic network conditions. The proposed method demonstrates improvements in serving probability of up to 27%, in comparison with greedy resource allocation schemes, and a several-orders-of-magnitude reduction in computation time, compared to the linear programming approach, which significantly reduces the service-interrupted user percentage when operating under variable network conditions.

• Journal of Information Processing Systems
• /
• 제17권3호
• /
• pp.489-504
• /
• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• 대한전기학회논문지:전력기술부문A
• /
• 제53권1호
• /
• pp.67-72
• /
• 2004

This paper addresses the bidding strategies of generating firms in a competitive market where the firms are provided with payment for generating reactive power. Reactive support for voltage control is an integral and critical part of power system operations. Since reactive support is unbundled in a competitive market under open access transmission, it is treated as one of ancillary services. The operation costs and opportunity costs for reactive support are compensated by payment to the firms, hence their bidding strategies will be affected. The opportunity costs are evaluated from the foregone profits of a generator in making sales in real power market by providing reactive support instead of real power. Game theory approach is used to analysis the transaction strategies of real power by the bimatrix method in this paper. Through computing the Nash equilibrium in a sample system, an incentive of a generator for improving the reactive generating capacity is found to be effective and the variations of the profits are analyzed as the demand power factor changes.

• Asian Pacific Journal of Cancer Prevention
• /
• 제15권18호
• /
• pp.7499-7503
• /
• 2014

Physical inactivity has reached epidemic levels in developed countries and is being recognized as a serious public health problem. Recent evidence shows a high percentages of individuals worldwide who are physically inactive, i.e. do not achieve the WHO's present recommendation of 150 minutes of moderate to vigorous intensity per week in addition to usual activities. Living in sedentary lifestyle is one of the leading causes of deaths and a high risk factor for several chronic diseases, like cancer, cardiovascular disease, diabetes type 2, and osteoporosis. This article summarizes evidence for relative risk of the civilization diseases attributable to physical inactivity and the most important conclusions available from the recent investigations computing the economic costs specific to physical inactivity. The findings provide health and economic arguments needed for people to understand the meaning of a sedentary lifestyle. This may be also useful for public health policy in the creation of programmes for prevention of physical inactivity.

• 인터넷정보학회논문지
• /
• 제17권1호
• /
• pp.7-14
• /
• 2016

최근 클라우드 컴퓨팅 기술이 발전함에 따라, 언제 어디서나 스마트 폰이나 컴퓨터로 접속하여 업무를 처리할 수 있다. 또한 IT 인프라를 구축하기 위한 초기투자비용과 유지보수에 대한 부담을 줄이는 방안으로 적합하다고 여겨지면서 클라우드 컴퓨팅은 발전하였다. 클라우드 컴퓨팅의 수요가 급격하게 늘어남에 따라, 데이터센터의 환경을 유지하기 위해 소비되는 전력에 관한 문제가 발생하였다. 이 문제를 해결하기 위해서는 먼저 소비전력을 측정할 수 있어야 한다. 비록 전력측정기를 이용하여 소비전력을 측정하는 것은 정확한 소비전력을 얻을 수 있지만, 추가비용이 발생한다. 따라서 본 논문에서는 전력측정기에 의존하지 않고 소비전력을 예측하는 방안을 제시한다. 제시한 방안의 정확성을 입증하기 위해 클라우드 컴퓨팅 환경에서 CPU와 Hard disk 테스트를 실시하였다. 테스트가 진행되는 동안, 제안한 방안과 전력측정기에 의해 예측 값과 실제 값을 얻고. 오차율을 계산하였다. 그 결과 CPU 테스트에서 예측 값과 실제 값의 차이는 약 4.22%이고, Hard disk 테스트에서는 약 8.51%을 보였다.

• 한국멀티미디어학회논문지
• /
• 제24권1호
• /
• pp.75-94
• /
• 2021

The 4th industrial revolution needs a fusion of artificial intelligence, robotics, the Internet of Things (IoT), edge computing, and other technologies. For the fusion of technologies, cloud computing technology can provide flexible and high-performance computing resources so that cloud computing can be the foundation technology of new emerging services. The emerging services become a global-scale, and require much higher performance, availability, and reliability. Public cloud providers already provide global-scale services. However, their services, costs, performance, and policies are different. Enterprises/ developers to come out with a new inter-operable service are experiencing vendor lock-in problems. Therefore, multi-cloud technology that federatively resolves the limitations of single cloud providers is required. We propose a software platform, denoted as Cloud-Barista. Cloud-Barista is a multi-cloud service common platform for federating multiple clouds. It makes multiple cloud services as a single service. We explain the functional architecture of the proposed platform that consists of several frameworks, and then discuss the main design and implementation issues of each framework. To verify the feasibility of our proposal, we show a demonstration which is to create 18 virtual machines on several cloud providers, combine them as a single resource, and manage it.

• 한국주조공학회지
• /
• 제31권4호
• /
• pp.218-223
• /
• 2011

Computer Aided Engineering (CAE) is very helpful field for every manufacturing industry including foundry. It covers CAD, CAM, and simulation technology also, and becomes as common sense in developing new products and processes. In South Korea, more than 600 foundries exist, and their average employee number is less than 40. Moreover, average age of them becomes higher. To break out these situations of foundry, software tools can be effective, and many commercial software tools had already been introduced. But their high costs and risks of investment act as difficulties in introducing the software tools to SMEs (Small and Medium size Enterprise). So we had developed cloud computing platform to propagate the CAE technologies to foundries. It includes HPC (High Performance Computing), platforms and software. So that users can try, enjoy, and utilize CAE software at cyber space without any investment. In addition, we also developed platform APIs (Application Programming Interface) to import not only our own CAE codes but also 3rd-party's packages to our cloud-computing platforms. As a result, CAE developers can upload their products on cloud platforms and distribute them through internet.

• Journal of Information Processing Systems
• /
• 제8권4호
• /
• 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.