• Journal of Information Processing Systems
• /
• v.18 no.6
• /
• pp.741-754
• /
• 2022

Openstack is widely used as a representative open-source infrastructure of the service (IaaS) platform. The Openstack Identity Service is a centralized approach component based on the token including the Memcached for cache, which is the in-memory key-value store. Token validation requests are concentrated on the centralized server as the number of differently encrypted tokens increases. This paper proposes the practical Byzantine fault tolerance (PBFT) blockchain-based Openstack Identity Service, which can improve the performance efficiency and reduce security vulnerabilities through a PBFT blockchain framework-based decentralized approach. The experiment conducted by using the Apache JMeter demonstrated that latency was improved by more than 33.99% and 72.57% in the PBFT blockchain-based Openstack Identity Service, compared to the Openstack Identity Service, for 500 and 1,000 differently encrypted tokens, respectively.

• Journal of Information Processing Systems
• /
• v.8 no.2
• /
• pp.241-262
• /
• 2012

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.

• KIISE Transactions on Computing Practices
• /
• v.21 no.3
• /
• pp.173-182
• /
• 2015

Motion control systems are widely deployed in various industrial automation processes. The motion controller, which is a key element of motion control systems, has stringent real-time constraints. The controller must provide a short and deterministic control message transmission cycle, and minimize the actuation deviation among motor drives. To meet these requirements, hardware-based proprietary controllers have been prevalent. However, since it is becoming difficult for such an approach to meet increasing needs of system interoperability and scalability, nowadays, software-based universal motion controllers are regarded as their substitutes. Recently, embedded motion controller solutions are gaining attention due to low cost and relatively high performance. In this paper, we designed and implemented an embedded motion controller on an ARM-based evaluation board by using Xenomai real-time kernel and other open source software components. We also measured and analyzed the performance of our embedded controller under a realistic test-bed environment. The experimental results show that our embedded motion controller can provide relatively deterministic performance with synchronized control of three motor axis at 2 ms control cycle.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.5
• /
• pp.1146-1154
• /
• 2010

A virtual machine lease service in the cloud computing environment has been implemented using the open source cloud computing platform, OpenNebula. In addition, a web-based cloud user interface is developed for both convenient resource management and efficient service access. The present virtual machine allocation scheme adopted in OpenNebula has performance reduction problem because of not considering CPU allocation scheduler of the virtualization software. In order to address this problem we have considered both the priority of the idle CPU resources of the cluster and credit scheduler of Xen, which resulted in performance improvement of the OpenNebula virtual machine scheduler. The experimental results showed that the proposed allocation scheme provided more virtual machine creations and more CPU resource allocations for cloud service.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.445-447
• /
• 2016

SQLite, Realm, etc. are database engines which are suitable for small devices that have large resource restriction and are used a lot in mobile devices based on Linux. These engines are open source programs and have advantages which are lighter weight and faster speed than general purpose databases. This paper implements performance test program and performs performance experiment of SQLite database to test search efficiency in Linux environments. The experiment is processed by comparison with the results of a Oracle database working out at the same environment.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.41 no.8
• /
• pp.553-561
• /
• 2017

The main motivation for this study was to establish a CFD-based procedure for the analysis of heating characteristics, particularly in industrial furnaces. As certain open-source software packages have gained popularity in dealing with complex industrial problems, the OpenFOAM framework was selected for further development of advanced physical models to meet industrial requirements. In this study, the newly developed comprehensive model was applied to simulate physical processes in the full-scale horizontal furnace of a continuous galvanizing line (CGL). The numerical results obtained indicate that the current approach predicts heating characteristics reasonably well. It was also found that radiative heat transfer plays a dominant role in heating the moving strip. To improve the predictability of our method, further work is required to model the turbulence-chemistry interaction realistically, as well as to impose a physically correct thermal wall boundary condition.

• KIISE Transactions on Computing Practices
• /
• v.23 no.4
• /
• pp.262-267
• /
• 2017

RTEMS is a real-time operating system using a GPLv2-based license, and is used in the aerospace industry, such as satellites. It is difficult to build a development environment for these special-purpose systems because of problems related to the hardware platform. This can be resolved by applying virtualization technology. However, increased execution time and performance degradation due to virtualization overhead can change timing behavior of real-time application, and needs to be minimized. In this paper, we aim to implement the network device driver of RTEMS for the standardized virtual network device called virtio in order to effectively utilize RTEMS in a virtualized environment. In addition, we describe the process of submitting implemented driver to the RTEMS community for contributing open source software and reflecting the various requirements of the community.

• Journal of The Korean Association of Information Education
• /
• v.14 no.4
• /
• pp.659-666
• /
• 2010

Performance assessment is recognized as a proper evaluation method to integrate the teaching and learning process into the evaluation one and as an essential means to produce meaningful evaluation results. However, it has deteriorated into a teacher-centric evaluation method with the students excluded due to the cost and time issues and the heavy workload on the teachers. Those changes have affected the reliability of the assessment results. This study developed and applied a web-based performance assessment system by using Moodle, open source software, to help the teachers efficiently run performance assessment in school and the students increase their learning effects. As a result, it lifted the burden of system development from the shoulders of the teachers trying to do web-based performance assessment and solved the reliability issue raised in the old peer evaluation approach through diverse strategies offered by Moodle.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.673-676
• /
• 2017

Accompanying the Internet of Things (IoT) is a demand of advanced applications and services utilizing the potential of the IoT environment. Monitoring the environment for a provision of context-aware services to the human beings is one of the new trends in our future life. The IoTivity Cloud is one of the most notable open-source platform bringing an opportunity to collect, analyze, and interpret a huge amount of data available in the IoT environment. Based on the IoTivity Cloud, we aim to develop a novel platform for comprehensive monitoring of a future network, which facilitates on-demand data collection to enable the network behavior prediction and the quality of user experience maintenance. In consideration of performance evaluation of the monitoring platform, this paper presents results of a preliminary test on the data acquisition/supply process in the IoTivity Cloud.

• Journal of the Korea Convergence Society
• /
• v.12 no.9
• /
• pp.11-19
• /
• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.