• Journal of Information Processing Systems
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• v.17 no.3
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• pp.489-504
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• 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.

• ETRI Journal
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• v.42 no.5
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• pp.658-668
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• 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 Technology Applications and Management
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• v.12 no.4
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• pp.173-195
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• 2005

As the amount of informatization investment is rapidly increasing in many organizations, it becomes more inevitable to manage computing resources (information systems, computing facilities and manpower etc.) effectively. Specially, in public sector It is thought to be very important to achieve the economy of scale by integrating computing resources which are managed individually in many agencies. Also, our government have been recently making much efforts to raise the effectiveness of operating the information systems by promoting joint information use among many public agencies, enhancing the operating systems and the expertise of IS staff and applying the optimal information security systems. This study focuses on presenting the framework to effectively integrate omputing resources and proposing the ways of constructing and operating the integrated computing environment for the institutions and the affiliated groups under the Ministry of Culture & Tourism which are in charge of implementing cultural informatization. The main implications of this study are 1) building the ideal model of the integrated computing environment architecture suitable to cultural informatization area, 2) showing the criteria of deciding whether the organizations use the Integrated computing environment or not and how extensively they commit their computing resources to it, and 3) suggesting the ways of the phased integration and the change management to minimize confusion in the process of adopting the integrated computing environment and behavioral problems such as conflict and resistance of IS-related Personnel Influenced by Implementing the integrated computing environment.

• Journal of the Korea Society of Computer and Information
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• v.9 no.4 s.32
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• pp.221-231
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• 2004

Ubiquitous computing is not radical as Mark Weiser argues and includes things. The status of each nation is as follows. Project of America or Europe are practice for computing infra. intelligence of things and space and man-computer co-relation study, otherwise Japan centers the studying of network ubiquity utilizing computing competence. In this thesis I analyzed real status on the ubiquitous computing case study of each nation. and I proposed computing development model. factor, structured value system. five type business and technology model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3663-3679
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• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.181-186
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• 2014

Web data mining aims at discovering useful knowledge from various Web resources. There is a growing trend among companies, organizations, and individuals alike of gathering information through Web data mining to utilize that information in their best interest. In science, cloud computing is a synonym for distributed computing over a network; cloud computing relies on the sharing of resources to achieve coherence and economies of scale, similar to a utility over a network, and means the ability to run a program or application on many connected computers at the same time. In this paper, we propose a new system framework based on the Hadoop platform to realize the collection of useful information of Web resources. The system framework is based on the Map/Reduce programming model of cloud computing. We propose a new data mining algorithm to be used in this system framework. Finally, we prove the feasibility of this approach by simulation experiment.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.563-570
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• 2004

Ubiquitous computing environment requires strong security and privacy. Access control is one of security areas. Access control on Ubiquitous computing is different from it on traditional information systems so that traditional access control models are not suitable for Ubiquitous comput-ing environment. This research defines Ubiquitous computing environment as an access control point of view, and shows requirements to consid-er for developing access control model for Ubiquitous computing environment. It also brings up three implementing types of access control on Ubiquitous computing environment.

• Knowledge Management Research
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• v.16 no.1
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• pp.117-142
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• 2015

Recently, the personal cloud computing service has been being spotlighted as an individual tool of productivity enhancement. However, compared to the rosy forecast, its diffusion rate in the domestic (Korean) market is much slower than expected. In order to find the reason for the slow growth of personal cloud computing service, we attempt to identify influencing factors on user's adoption resistance, while most prior research has focused on the factors affecting its adoption. Based on both the person-technology fit model and the privacy calculus model, we propose technostress and perceived value as key antecedents of adoption resistance. In addition, we identify (1) technical (pace of change and complexity) and personal (self-efficacy) influencing factors on technostress, and (2) beneficial (perceived mobility and perceived availability) and harmful (perceived vulnerability) influencing factors on perceived value. To validate our research model, 133 individual samples were gathered from undergraduate and graduate students who had actual experience of using at least one of personal cloud computing services. The results of the structural equation modeling confirm that both technostress and perceived value have significant effects on adoption resistance, but they have different influencing mechanisms to different types of adoption resistance (indifference, postponement, and rejection). Theoretical and practical contributions are discussed in the conclusion.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.87-94
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• 2006

Grid computing has been developed for resolving large-scaled computing problems through geographically distributed heterogeneous resources. In order to guarantee effective and reliable job processing, grid computing needs resource scheduling model. So, we propose a resource performance measurement scheduling model which allocates job to resources with resource performance measurement. We assess resources using resource performance measurement formula, and implement the resource performance measurement scheduling model in DEVS simulation modeling.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.4
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• pp.1-7
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• 2001

Remotely sensed data is the most fundamental data in acquiring the GIS informations, and may be analyzed to extract useful thematic information. Multi-spectral classification is one of the most often used methods of information extraction. The actual multi-spectral classification may be performed using either supervised or unsupervised approaches. This paper analyze the effect of assigning clever initial values to image classes on the performance of parallelepiped classification algorithm, which is one of the supervised classification algorithms. First, we investigate the effect on serial computing model, then expand it on MIMD(Multiple Instruction Multiple Data) parallel computing model. On serial computing model, the performance of the parallel pipe algorithm improved 2.4 times at most and, on MIMD parallel computing model the performance improved about 2.5 times as clever initial values are assigned to image class. Through computer simulation we find that initial values of image class greatly affect the performance of parallelepiped classification algorithms, and it can be improved greatly when classes on both serial computing model and MIMD parallel computation model.