• ETRI Journal
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• v.36 no.5
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• pp.741-751
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• 2014

Smart TV is expected to bring cloud services based on virtualization technologies to the home environment with hardware and software support. Although most physical resources can be shared among virtual machines (VMs) using a time sharing approach, allocating the proper amount of memory to VMs is still challenging. In this paper, we propose a novel mechanism to dynamically balance the memory allocation among VMs in virtualized Smart TV systems. In contrast to previous studies, where a virtual machine monitor (VMM) is solely responsible for estimating the working set size, our mechanism is symbiotic. Each VM periodically reports its memory usage pattern to the VMM. The VMM then predicts the future memory demand of each VM and rebalances the memory allocation among the VMs when necessary. Experimental results show that our mechanism improves performance by up to 18.28 times and reduces expensive memory swapping by up to 99.73% with negligible overheads (0.05% on average).

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2006.06a
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• pp.251-252
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• 2006

This study developed the virtual operation system for the hydraulic pump system for marine usage. The scope of this study is to develop a process dynamic simulation model for the hydraulic pump system for marine usage, to investigate the process dynamic characteristics using the models, to accomplish the logic diagram for the PLC control and to achieve a human-machine interface (HMI) for the convenience of operators to monitor and control the process. The virtual operation system provides a virtual operation environment for the pumping system, enabling the operators to simulate the change of process variables. The system will assist in developing advanced control logics and then optimal design of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2801-2815
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• 2022

With the advent and realization of Software Defined Network (SDN) architecture, many organizations are now shifting towards this paradigm. SDN brings more control, higher scalability, and serene elasticity. The SDN spontaneously changes the network configuration according to the dynamic network requirements inside the constrained environments. Therefore, a monitoring system that can monitor the physical and virtual entities is needed to operate this type of network technology with high efficiency and proficiency. In this manuscript, we propose a real-time monitoring system for data collection and visualization that includes the Prometheus, node exporter, and Grafana. A node exporter is configured on the physical devices to collect the physical and virtual entities resources utilization logs. A real-time Prometheus database is configured to collect and store the data from all the exporters. Furthermore, the Grafana is affixed with Prometheus to visualize the current network status and device provisioning. A monitoring system is deployed on the physical infrastructure of the KOREN topology. Data collected by the monitoring system is further pre-processed and restructured into a dataset. A monitoring system is further enhanced by including machine learning techniques applied on the formatted datasets to identify the elephant flows. Additionally, a Random Forest is trained on our generated labeled datasets, and the classification models' performance are verified using accuracy metrics.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• The KIPS Transactions:PartC
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• v.18C no.3
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• pp.157-166
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• 2011

The virtualization layer is executed in higher authority layer than kernel layer and suitable for monitoring operating systems. However, existing virtualization monitoring systems provide simple information about the usage rate of CPU or memory. In this paper, the monitoring system using full virtualization technique is proposed, which can monitor virtual machine's dynamic kernel object as memory, register, GDT, IDT and system call table. To verify the monitoring system, the proposed system was implemented based on KVM(Kernel-based Virtual Machine) with full virtualization that is directly applied to linux kernel without any modification. The proposed system consists of KvmAccess module to access KVM's internal object and API to provide other external modules with monitoring result. In experiments, the CPU utilization for monitoring operations in the proposed monitering system is 0.35% when the system is monitored with 1-second period. The proposed monitoring system has a little performance degradation.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.520-523
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• 2011

가상화는 하나의 컴퓨팅 노드에 여러 개의 가상 머신을 구성하여 서버의 자원 활용률을 높여주는 매우 유용한 기술이다. 하지만 아직까지 멀티코어 기반의 전가상화(Full Virtualization) 환경에서 네트워크 I/O 성능 향상을 위한 연구는 미비하다. 또한 기존의 프로세스 스케줄링 기법은 통신을 수행하는 게스트 도메인의 프로세스들을 효과적으로 지원해주지 않는다. 이러한 문제를 해결하기 위하여 네트워크 I/O 가상화를 위한 통신 프로세스의 동적 스케줄링 방식을 제안한다. 기존의 프로세스 친화도 결정 기법을 기반으로 네트워크 I/O 가상화에 특화된 제안 기법은 전가상화 VMM(Virtual Machine Monitor)인 VirtualBox를 대상으로 구현되었으며, 성능 측정을 통하여 네 개의 가상 머신을 적용하였을 경우 기존 리눅스 스케줄러 대비 총 네트워크 사용량을 약 97% 상승 시킴을 보인다.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.122-130
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• 2019

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6098-6122
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• 2018

Cache-based side-channel attacks have achieved more attention along with the development of cloud computing technologies. However, current host-based mitigation methods either provide bad compatibility with current cloud infrastructure, or turn out too application-specific. Besides, they are defending blindly without any knowledge of on-going attacks. In this work, we present CacheSCDefender, a framework that provides a (Virtual Machine Monitor) VMM-based comprehensive defense framework against all levels of cache attacks. In designing CacheSCDefender, we make three key contributions: (1) an attack-aware framework combining our novel dynamic remapping and traditional cache cleansing, which provides a comprehensive defense against all three cases of cache attacks that we identify in this paper; (2) a new defense method called dynamic remapping which is a developed version of random permutation and is able to deal with two cases of cache attacks; (3) formalization and quantification of security improvement and performance overhead of our defense, which can be applicable to other defense methods. We show that CacheSCDefender is practical for deployment in normal virtualized environment, while providing favorable security guarantee for virtual machines.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.146-148
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• 2012

임베디드 환경에서의 가상화 연구는 분산 되었던 노드들을 통합할 수 있게 해주면서도, 기존의 시스템 소프트웨어를 수정 없이 사용할 수 있다는 장점으로 인해 각광 받고 있다. 하지만 기존 VMM(Virtual Machine Monitor)의 I/O 가상화 기술은 임베디드 환경에 바로 적용하기에는 비효율적인 구조를 취하고 있다. 본 논문에서는 임베디드 시스템을 위한 전가상화 VMM인 ViMo를 기반으로 virtio를 적용하여 기존 VMM들의 I/O 가상화보다 효율적인 구조의 임베디드 I/O 가상화 기법을 제안한다.