• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.270-278
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• 2023

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.6
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• pp.1398-1417
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• 2013

A method is proposed to reduce excess resources from a virtual machine(VM) while avoiding subsequent migrations for a computer cluster that provides cloud service. The proposed scheme cuts down on the resources of a VM based on the probability that migration may occur after a reduction. First, it finds a VM that can be scaled down by analyzing the history of the resource usage. Then, the migration probability is calculated as a function of the VM resource usage trend and the trend error. Finally, the amount of resources needed to eliminate from an underutilized VM is determined such that the migration probability after the resource reduction is less than or equal to an acceptable migration probability. The acceptable migration probability, to be set by the cloud service provider, is a criterion to assign a weight to the resource reduction either to prevent VM migrations or to enhance VM utilization. The results of simulation show that the proposed scheme lowers migration frequency by 31.6~60.8% depending on the consistency of resource demand while losing VM utilization by 9.1~21.5% compared to other known approaches, such as the static and the prediction-based methods. It is also verified that the proposed scheme extends the elapsed time before the first occurrence of migration after resource reduction 1.1~2.3-fold. In addition, changes in migration frequency and VM utilization are analyzed with varying acceptable migration probabilities and the consistency of resource demand patterns. It is expected that the analysis results can help service providers choose a right value of the acceptable migration probability under various environments having different migration costs and operational costs.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.1
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• pp.35-42
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• 2010

Guest operating systems running over the virtual machines share a variety of resources. Since CPU is allocated in a time division manner it consequently leads them to having the unknown physical time. It is not regarded as a serious problem in the server virtualization fields. However, it becomes critical in embedded systems because it prevents guest OS from executing real time tasks when it does not occupy CPU. In this paper we propose a hypercall to register a timer service to notify the timer request related real time. It enables hypervisor to schedule a virtual machine which has real time tasks to execute, and allows guest OS to take CPU on time to support real time. The following experiment shows its implementation on Xen-Arm and para-virtualized Linux. We also analyze the real time performance with response time of test application and frames per second of Mplayer.

• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.161-168
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• 2012

The study of Cloud based system is emerging to become the core technology in IT field due to the tremendous growth of Cloud Computing. Researches to deliver applications to Thin-Client based mobile virtual machine and Desktop as a Service(DaaS) using Cloud Computing are conducted actively. In this paper, we propose a Cloud system to run the mobile application in the mobile Thin-Client device and resource allocation mechanism Dynamic Resource Allocation Manager for Mobile Application(DRAMMA). Thus, through performance check, we show DRAMMA has improved the utilization of Cloud system, less migration of virtual machines and decreased the error rate of resource allocation. Also our proposed system delivers service more efficiently than the previous resource allocation algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.93-98
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• 2022

Recently, fast storage media such as Optane have emerged, and memory system configurations designed for disk storage should be reconsidered. In this paper, we analyze the effect of the page size on the memory system performances when fast storage is adopted. Based on this, we design a page size model that can guide an appropriate page size for given workloads in virtualized environments. Configuring different page sizes for various workloads is not an easy matter in traditional systems, but due to the widespread adoption of cloud systems, page sizing performed in our model is feasible for virtual machines, which are generated for executing specific workloads. Simulation experiments under various virtual machine scenarios show that the proposed model improves the memory access time significantly by configuring page sizes for given workloads.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.107-116
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• 2024

In this paper, we analyze the performance of MicroVM when running AI applications on an edge computing environment and whether it can replace current container technology and traditional virtual machines. To achieve this, we set up Docker container, Firecracker MicroVM and KVM virtual machine environments on a Raspberry Pi 4 and executed representative AI applications in each environment. We analyze the inference time, total CPU usage and trends over time and file I/O performance on each environment. The results show that there is no significant performance difference between MicroVM and container when running AI applications. Moreover, on average, a stable inference time over multiple trials was observed on MicroVM. Therefore, we can confirm that executing AI applications using MicroVM instead of container or heavy-weight virtual machine is suitable for an edge computing.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.7
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• pp.591-596
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• 2014

Augmented reality is a novel human-machine interaction that overlays virtual computer-generated information on a real world environment. It has found good potential applications in many fields, such as training, surgery, entertainment, maintenance, assembly, product design and other manufacturing operations. In this study, a smartphone-based augmented reality system was developed for the purpose of monitoring and managing injection molding production lines. Required management items were drawn from a management content analysis, and then the items were divided into two broad management categories: line management and equipment management. Effective work management was enabled by providing those working on the shop floor with management content information combined with the actual images of an injection molding production line through augmented reality.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.3
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• pp.306-314
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• 2009

The pelvis platform is the mechanical part which accomplishes the activities of diminishing the disturbances from the lower body and maintaining a balanced posture. When a biped robot walks, a lot of disturbances and irregular vibrations are generated and transmitted to the upper body. As there are some important machines and instruments in the upper body or head such as CPU, controller units, vision system, etc., the upper part should be isolated from disturbances or vibrations to functions properly and finally to improve the biped stability. This platform has 3 rotational degrees of freedom and is able to maintain balanced level by feedback control system. Some sensors are fused for more accurate estimation and the control system which integrates synchronization and active filtering is simulated on the virtual environment.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.1
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• pp.32-37
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• 2007

The outer diameter finishing grinding process required for ferrules, which are widely used as fiber optic connectors, is carried out by high-precision centerless grinding machines. In this study, the thermal characteristics of such a machine, for example, the temperature distribution, temperature rise, and thermal deformation, were estimated based on a virtual prototype and the heat generation rates of heat sources related to normal operating conditions. The prototype consisted of a concrete-filled bed. hydrostatic grinding wheel (GW) and regulating wheel (RW) spindle systems, a hydrostatic RW feed mechanism, a RW swivel mechanism, and on-machine GW and RW dressers. The reliability of the predicted results was demonstrated using temperature characteristics measured from a physical prototype. The predicted and measured results indicated that this particular high-precision centerless grinding machine had very stable thermal characteristics.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.32-35
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• 2017

직접 통로(Direct Pass-through) 기반 GPU(Graphic Processing Unit) 가상화 기법은 클라우드 환경에서 가상머신에게 GPU 장치의 기능을 지원하기 위한 일반적인 방법 중 하나이다. GPU 장치는 GPGPU 기술을 통해 연산을 가속화 할 수 있기 때문에 클라우드 환경에서도 가상머신에 고성능 연산을 지원하기 위해 많이 사용되고 있다. 하지만 기존 가상머신 스케줄링 기법은 가상머신의 CPU 사용 시간을 기반으로 스케줄링 되며, GPU 자원 사용을 고려하지 않는다. 본 논문에서는 GPU와 CPU 연산을 수행하는 가상머신들이 동시에 실행되는 환경에서 성능 실험을 통해 가상머신의 GPU 연산이 다른 가상머신에게 미치는 성능 영향과 GPU 작업 길이가 다른 가상머신에게 미치는 영향을 분석한다.