• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2003.12a
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• pp.669-685
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• 2003

Nowadays mobile phones and PDAs are part and parcel of our lives. By carrying a portable mobile device with us all the time we are already living in partial Pervasive Computing Environment (PCE) that is waiting to be exploited very soon. One of the advantages of pervasive computing is that it strongly supports the deployment of Location-Based Service(s) (LBSs). In PCE, there would be many competitive service providers (SPs) trying to sell different or similar LBSs to users. In order to reserve a particular service, it becomes very difficult for a low-computing and resource-poor mobile device to handle many such SPs at a time, and to identify and securely communicate with only genuine ones. Our paper establishes a convincing trust model through which secure job delegation is accomplished. Secure Job delegation and cost effective cryptographic techniques largely help in reducing the burden on the mobile device to securely communicate with trusted SPs. Our protocol also provides users privacy protection, replay protection, entity authentication, and message authentication, integrity, and confidentiality. This paper explains our protocol by suggesting one of the LBSs namely“Secure Automated Taxi Calling Service”.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3523-3543
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• 2022

Autonomous vehicles are gradually being regarded as the mainstream trend of future development of the automobile industry. Autonomous driving networks generate many intensive and delay-sensitive computing tasks. The storage space, computing power, and battery capacity of autonomous vehicle terminals cannot meet the resource requirements of the tasks. In this paper, we focus on the task scheduling problem of autonomous driving in software-defined edge networks. By analyzing the intensive and delay-sensitive computing tasks of autonomous vehicles, we propose an emotion model that is related to task urgency and changes with execution time and propose an optimal base station (BS) task scheduling (OBSTS) algorithm. Task sentiment is an important factor that changes with the length of time that computing tasks with different urgency levels remain in the queue. The algorithm uses task sentiment as a performance indicator to measure task scheduling. Experimental results show that the OBSTS algorithm can more effectively meet the intensive and delay-sensitive requirements of vehicle terminals for network resources and improve user service experience.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3619-3637
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• 2022

Big data analytics offers endless opportunities for operational enhancement by extracting valuable insights from complex voluminous data. Hadoop is a comprehensive technological suite which offers solutions for the large scale storage and computing needs of Big data. The performance of Hadoop is closely tied with its configuration settings which depends on the cluster capacity and the application profile. Since Hadoop has over 190 configuration parameters, tuning them to gain optimal application performance is a daunting challenge. Our approach is to extract a subset of impactful parameters from which the performance enhancing sub-optimal configuration is then narrowed down. This paper presents a statistical model to analyze the significance of the effect of Hadoop parameters on a variety of performance metrics. Our model decomposes the total observed performance variation and ascribes them to the main parameters, their interaction effects and noise factors. The method clearly segregates impactful parameters from the rest. The configuration setting determined by our methodology has reduced the Job completion time by 22%, resource utilization in terms of memory and CPU by 15% and 12% respectively, the number of killed Maps by 50% and Disk spillage by 23%. The proposed technique can be leveraged to ease the configuration tuning task of any Hadoop cluster despite the differences in the underlying infrastructure and the application running on it.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.689-692
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• 2021

This paper is a prior study to improve the efficiency of offloading based on mobile agents to optimize allocation of computing resources and reduce latency that support user proximity of application services in a Fog/Edge Computing (FEC) environment. We propose an algorithm that effectively reduces the execution time and the amount of memory required when extracting optimal moving patterns from the vast set of spatio-temporal movement history data of moving objects. The proposed algorithm can be useful for the distribution and deployment of computing resources for computation offloading in future FEC environments through frequency-based optimal path extraction.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.153-160
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• 2024

Information technology plays an important role in healthcare. The cloud has several applications in the fields of education, social media and medicine. But the advantage of the cloud for medical reasons is very appropriate, especially given the large volume of data generated by healthcare organizations. As in increasingly health organizations adopting towards electronic health records in the cloud which can be accessed around the world for various health issues regarding references, healthcare educational research and etc. Cloud computing has many advantages, such as "flexibility, cost and energy savings, resource sharing and rapid deployment". However, despite the significant benefits of using the cloud computing for health IT, data security, privacy, reliability, integration and portability are some of the main challenges and obstacles for its implementation. Health data are highly confidential records that should not be made available to unauthorized persons to protect the security of patient information. In this paper, we discuss the privacy and security requirement of EHS as well as privacy and security issues of EHS and also focus on a comprehensive review of the current and existing literature on Electronic health that uses a variety of approaches and procedures to handle security and privacy issues. The strengths and weaknesses of some of these methods were mentioned. The significance of security issues in the cloud computing environment is a challenge.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.8
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• pp.245-252
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• 2015

Recently, parallel processing methods with accelerator have been introduced into a high performance computing and a mobile computing. The photomosaic application can be parallelized by using inherent data parallelism and accelerator. In this paper, we propose a way to distribute the workload of the photomosaic application into a CPU and GPU heterogeneous computing environment. That is, the photomosaic application is parallelized using both CPU and GPU resource with the asynchronous mode of OpenCL, and then the optimal workload distribution rate is estimated by measuring the execution time with CPU-only and GPU-only distribution rates. The proposed approach is simple but very effective, and can be applied to parallelize other applications on a CPU and GPU heterogeneous computing environment. Based on the experimental results, we confirm that the performance is improved by 141% into a heterogeneous computing environment with the optimal workload distribution compared with using GPU-only method.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.167-173
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• 2017

As the increasing use of the cloud computing, virtualization technology have been combined and applied a variety of requirements. Cloud computing has the advantage that the support computing resource by a flexible and scalable to users as they want and it utilized in a variety of distributed computing. To do this, it is especially important to ensure the stability of the cloud computing. In this paper, we analyzed a variety of component measurement using open-source tools for ensuring the performance of the system on the education system to build cloud testbed environment. And we extract the performance that may affect the virtualization environment from processor, memory, cache, network, etc on each of the host machine(Host Machine) and a virtual machine (Virtual Machine). Using this result, we can clearly grasp the state of the system and also it is possible to quickly diagnose the problem. Furthermore, the cloud computing can be guaranteed the SLA(Service Level Agreement).

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.149-157
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• 2008

In the field of computing service, there is a copernican revolution indebted to development of network & computer technology. Computer system, which is set to mainframe in the 1960's, is advancing torwards to the paradigm of server based computing, so-called thin-client. In thin-client computing, network is the platform which is responsible for transfer of application, so that client execute application installed on server. It is also possible that each system share the computing resource connected with network. In this parer, we suggest component & distributed computing technology as a measn for the implementation of thin-client architecture hence, make the best use of COM(Component Object Model and PYRO(PYthon Remote Objects). We talk about the concept and mechanism of thin-client at the beginning, and propose the design of network architecture for the implementation thin-client.

• Journal of Service Research and Studies
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• v.2 no.2
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• pp.91-98
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• 2012

The emergence of new types of embedded computing devices and developments in wireless networking are broadening the domain of computing from the work place and home office to other facets of everyday life. This trend is expected to lead to a proliferation of Internet of Things (IoT) environments, in which inexpensive and interconnected computing devices are capable of supporting users in a range of tasks. Context-aware computing is a key source to develop such smart services. However, there are many challenges to enable services to be smart; Heterogeneous Computing Environments, Resource Limitations, Large Amount of Data Produced, and Different Requirements for Context Interpretation. Because of these challenges, there are difficulties in providing smart service by utilizing IoT devices. Currently, many researchers are conducting researches on mobile-computing based smart service development and provisioning and network infrastructure for interconnected IoT devices. Still, there are some limitations on developing core technologies for IoT computing based smart service development. In order to remedy this situation, this thesis presents a reusable framework that provides unique and noble features which are required in developing advanced context-aware IoT applications.

• Convergence Security Journal
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• v.13 no.1
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• pp.37-43
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• 2013

In 2010, the Ministry of National Defense decided to build a MegaCenter including the cloud computing technology by 2014, as part of the '2012 Information Service Plan', which is now underway. The Cloud computing system environment should be designed applying cloud computing technology and policy for an efficient infrastructure that many IT resources are available in the data center as a concentrated form. That is, the system should be designed in such a way that clouding services will be efficiently provided to meet the needs of users and there will not be unnecessary waste of resources. However, in order to build an optimal system, it should be possible to predict the service performance and the resource availability at the initial phase of system design. In this paper, using the CloudAnalyst simulator to predict availability of the K-defence cloud computing system service, conducts cloud modeling and analysis of the 'Facebook', one of the most famous social network service applications with most users in the world. An Optimal K-Defense cloud computing design model is proposed through simulation results.