• Title/Summary/Keyword: Virtual Machine

Search Result 864, Processing Time 0.033 seconds

Performance Analysis of Docker Container Migration Using Secure Copy in Mobile Edge Computing (모바일 엣지 컴퓨팅 환경에서 안전 복사를 활용한 도커 컨테이너 마이그레이션 성능 분석)

  • Byeon, Wonjun;Lim, Han-wool;Yun, Joobeom
    • Journal of the Korea Institute of Information Security & Cryptology
    • /
    • v.31 no.5
    • /
    • pp.901-909
    • /
    • 2021
  • Since mobile devices have limited computational resources, it tends to use the cloud to compute or store data. As real-time becomes more important due to 5G, many studies have been conducted on edge clouds that computes at locations closer to users than central clouds. The farther the user's physical distance from the edge cloud connected to base station is, the slower the network transmits. So applications should be migrated and re-run to nearby edge cloud for smooth service use. We run applications in docker containers, which is independent of the host operating system and has a relatively light images size compared to the virtual machine. Existing migration studies have been experimented by using network simulators. It uses fixed values, so it is different from the results in the real-world environment. In addition, the method of migrating images through shared storage was used, which poses a risk of packet content exposure. In this paper, Containers are migrated with Secure CoPy(SCP) method, a data encryption transmission, by establishing an edge computing environment in a real-world environment. It compares migration time with Network File System, one of the shared storage methods, and analyzes network packets to verify safety.

Construction of a Sub-catchment Connected Nakdong-gang Flood Analysis System Using Distributed Model (분포형 모형을 이용한 소유역 연계 낙동강 홍수해석시스템 구축)

  • Choi, Yun-Seok;Won, Young-Jin;Kim, Kyung-Tak
    • Proceedings of the Korea Water Resources Association Conference
    • /
    • 2018.05a
    • /
    • pp.202-202
    • /
    • 2018
  • 본 논문에서는 분포형 강우-유출 모형인 GRM(Grid based Rainfall-runoff Model)(최윤석, 김경탁, 2017)을 이용해서 낙동강 유역을 대상으로 대유역 홍수해석시스템을 구축하고, 유출해석을 위한 실행시간을 평가하였다. 유출모형은 낙동강의 주요 지류와 본류를 소유역으로 구분하여 모형을 구축하고, 각 소유역의 유출해석 결과를 실시간으로 연계할 수 있도록 하여 낙동강 전체 유역의 유출모형을 구축하였다. 이와 같이 하나의 대유역을 다수의 소유역시스템으로 분할하여 모형을 구축할 경우, 유출해석시스템 구성이 복잡해지는 단점이 있으나, 소유역별로 각기 다른 자료를 이용하여 다양한 해상도로 유출해석을 할 수 있으므로, 소유역별 특성에 맞는 유출모형 구축이 가능한 장점이 있다. 또한 각 소유역시스템은 별도의 프로세스로 계산이 진행되므로, 대유역을 고해상도로 해석하는 경우에도 계산시간을 단축할 수 있다. 본 연구에서는 낙동강 유역을 20개(본류 구간 3개, 1차 지류 13개, 댐상류 4개)의 소유역으로 분할하여 계산 시간을 검토하였으며, 최종적으로 21개(본류 구간 3개, 1차 지류 13개, 댐상류 5개)의 소유역으로 분할하여 유출해석시스템을 구축하였다. 댐 상류 유역은 댐하류와 유량전달이 없이 독립적으로 모의되고, 댐과 연결된 하류 유역은 관측 방류량을 상류단 하천의 경계조건으로 적용한다. 지류 유역은 본류 구간과 연결되고, 지류의 계산 유량은 본류와의 연결지점에 유량조건으로 실시간으로 입력된다. 이때 본류와 지류의 유량 연계는 데이터베이스를 매개로 하였다. 유출해석시스템의 성능을 평가하기 위해서 Microsoft 클라우드 서비스인 Azure를 이용하였다. 낙동강 유역을 20개 소유역으로 구성한 경우에서의 유출해석시스템의 속도 평가 결과 Azure virtual machine instance DS15 v2(OS : Windows Server 2012 R2, CPU : 2.4 GHz Intel $Xeon^{(R)}$ E5-2673 v3 20 cores)에서 1.5분이 소요 되었다. 계산시간 평가시 GRM은 'IsParallel=false' 옵션을 적용하였으며, 모의 기간은 24시간을 기준으로 하였다. 연구결과 분포형 모형을 이용한 대유역 유출해석시스템 구축이 가능했으며, 계산시간도 충분히 단축할 수 있었다. 또한 추가적인 CPU와 병렬계산을 적용할 경우, 계산시간은 더 단축될 수 있으며, 이러한 기법들은 분포형 모형을 이용한 대유역 유출해석시스템 구축시 유용하게 활용될 수 있을 것으로 판단된다.

  • PDF

An Investigation on Digital Humanities Research Trend by Analyzing the Papers of Digital Humanities Conferences (디지털 인문학 연구 동향 분석 - Digital Humanities 학술대회 논문을 중심으로 -)

  • Chung, EunKyung
    • Journal of the Korean Society for Library and Information Science
    • /
    • v.55 no.1
    • /
    • pp.393-413
    • /
    • 2021
  • Digital humanities, which creates new and innovative knowledge through the combination of digital information technology and humanities research problems, can be seen as a representative multidisciplinary field of study. To investigate the intellectual structure of the digital humanities field, a network analysis of authors and keywords co-word was performed on a total of 441 papers in the last two years (2019, 2020) at the Digital Humanities Conference. As the results of the author and keyword analysis show, we can find out the active activities of Europe, North America, and Japanese and Chinese authors in East Asia. Through the co-author network, 11 dis-connected sub-networks are identified, which can be seen as a result of closed co-authoring activities. Through keyword analysis, 16 sub-subject areas are identified, which are machine learning, pedagogy, metadata, topic modeling, stylometry, cultural heritage, network, digital archive, natural language processing, digital library, twitter, drama, big data, neural network, virtual reality, and ethics. This results imply that a diver variety of digital information technologies are playing a major role in the digital humanities. In addition, keywords with high frequency can be classified into humanities-based keywords, digital information technology-based keywords, and convergence keywords. The dynamics of the growth and development of digital humanities can represented in these combinations of keywords.

Development and Validation of Digital Twin for Analysis of Plant Factory Airflow (식물공장 기류해석을 위한 디지털트윈 개발 및 실증)

  • Jeong, Jin-Lip;Won, Bo-Young;Yoo, Ho-Dong;Kim, Tag Gon;Kang, Dae-Hyun;Hong, Kyung-Jin
    • Journal of the Korea Society for Simulation
    • /
    • v.31 no.1
    • /
    • pp.29-41
    • /
    • 2022
  • As one of the alternatives to solve the problem of unstable food supply and demand imbalance caused by abnormal climate change, the need for plant factories is increasing. Airflow in plant factory is recognized as one of important factor of plant which influence transpiration and heat transfer. On the other hand, Digital Twin (DT) is getting attention as a means of providing various services that are impossible only with the real system by replicating the real system in the virtual world. This study aimed to develop a digital twin model for airflow prediction that can predict airflow in various situations by applying the concept of digital twin to a plant factory in operation. To this end, first, the mathematical formalism of the digital twin model for airflow analysis in plant factories is presented, and based on this, the information necessary for airflow prediction modeling of a plant factory in operation is specified. Then, the shape of the plant factory is implemented in CAD and the DT model is developed by combining the computational fluid dynamics (CFD) components for airflow behavior analysis. Finally, the DT model for high-accuracy airflow prediction is completed through the validation of the model and the machine learning-based calibration process by comparing the simulation analysis result of the DT model with the actual airflow value collected from the plant factory.

Active VM Consolidation for Cloud Data Centers under Energy Saving Approach

  • Saxena, Shailesh;Khan, Mohammad Zubair;Singh, Ravendra;Noorwali, Abdulfattah
    • International Journal of Computer Science & Network Security
    • /
    • v.21 no.11
    • /
    • pp.345-353
    • /
    • 2021
  • Cloud computing represent a new era of computing that's forms through the combination of service-oriented architecture (SOA), Internet and grid computing with virtualization technology. Virtualization is a concept through which every cloud is enable to provide on-demand services to the users. Most IT service provider adopt cloud based services for their users to meet the high demand of computation, as it is most flexible, reliable and scalable technology. Energy based performance tradeoff become the main challenge in cloud computing, as its acceptance and popularity increases day by day. Cloud data centers required a huge amount of power supply to the virtualization of servers for maintain on- demand high computing. High power demand increase the energy cost of service providers as well as it also harm the environment through the emission of CO2. An optimization of cloud computing based on energy-performance tradeoff is required to obtain the balance between energy saving and QoS (quality of services) policies of cloud. A study about power usage of resources in cloud data centers based on workload assign to them, says that an idle server consume near about 50% of its peak utilization power [1]. Therefore, more number of underutilized servers in any cloud data center is responsible to reduce the energy performance tradeoff. To handle this issue, a lots of research proposed as energy efficient algorithms for minimize the consumption of energy and also maintain the SLA (service level agreement) at a satisfactory level. VM (virtual machine) consolidation is one such technique that ensured about the balance of energy based SLA. In the scope of this paper, we explore reinforcement with fuzzy logic (RFL) for VM consolidation to achieve energy based SLA. In this proposed RFL based active VM consolidation, the primary objective is to manage physical server (PS) nodes in order to avoid over-utilized and under-utilized, and to optimize the placement of VMs. A dynamic threshold (based on RFL) is proposed for over-utilized PS detection. For over-utilized PS, a VM selection policy based on fuzzy logic is proposed, which selects VM for migration to maintain the balance of SLA. Additionally, it incorporate VM placement policy through categorization of non-overutilized servers as- balanced, under-utilized and critical. CloudSim toolkit is used to simulate the proposed work on real-world work load traces of CoMon Project define by PlanetLab. Simulation results shows that the proposed policies is most energy efficient compared to others in terms of reduction in both electricity usage and SLA violation.

Performance Evaluation of Loss Functions and Composition Methods of Log-scale Train Data for Supervised Learning of Neural Network (신경 망의 지도 학습을 위한 로그 간격의 학습 자료 구성 방식과 손실 함수의 성능 평가)

  • Donggyu Song;Seheon Ko;Hyomin Lee
    • Korean Chemical Engineering Research
    • /
    • v.61 no.3
    • /
    • pp.388-393
    • /
    • 2023
  • The analysis of engineering data using neural network based on supervised learning has been utilized in various engineering fields such as optimization of chemical engineering process, concentration prediction of particulate matter pollution, prediction of thermodynamic phase equilibria, and prediction of physical properties for transport phenomena system. The supervised learning requires training data, and the performance of the supervised learning is affected by the composition and the configurations of the given training data. Among the frequently observed engineering data, the data is given in log-scale such as length of DNA, concentration of analytes, etc. In this study, for widely distributed log-scaled training data of virtual 100×100 images, available loss functions were quantitatively evaluated in terms of (i) confusion matrix, (ii) maximum relative error and (iii) mean relative error. As a result, the loss functions of mean-absolute-percentage-error and mean-squared-logarithmic-error were the optimal functions for the log-scaled training data. Furthermore, we figured out that uniformly selected training data lead to the best prediction performance. The optimal loss functions and method for how to compose training data studied in this work would be applied to engineering problems such as evaluating DNA length, analyzing biomolecules, predicting concentration of colloidal suspension.

Understanding the Evaluation of Quality of Experience for Metaverse Services Utilizing Text Mining: A Case Study on Roblox (텍스트마이닝을 활용한 메타버스 서비스의 경험 품질 평가의 이해: 로블록스 사례 연구)

  • Minjun Kim
    • Journal of Service Research and Studies
    • /
    • v.13 no.4
    • /
    • pp.160-172
    • /
    • 2023
  • The metaverse, derived from the fusion of "meta" and "universe," encompasses a three-dimensional virtual realm where avatars actively participate in a range of political, economic, social, and cultural activities. With the recent development of the metaverse, the traditional way of experiencing services is changing. While existing studies have mainly focused on the technological advancements of metaverse services (e.g., scope of technological enablers, application areas of technologies), recent studies are focusing on evaluating the quality of experience (QoE) of metaverse services from a customer perspective. This is because understanding and analyzing service characteristics that determine QoE from a customer perspective is essential for designing successful metaverse services. However, relatively few studies have explored the customer-oriented approach for QoE evaluation thus far. This study conducted an online review analysis using text mining to overcome this limitation. In particular, this study analyzed 227,332 online reviews of the Roblox service, known as a representative metaverse service, and identified points for improving the Roblox service based on the analysis results. As a result of the study, nine service features that can be used for QoE evaluation of metaverse services were derived, and the importance of each feature was estimated through relationship analysis with service satisfaction. The importance estimation results identified the "co-experience" feature as the most important. These findings provide valuable insights and implications for service companies to identify their strengths and weaknesses, and provide useful insights to gain an advantage in the changing metaverse service environment.

Home Economics Teachers' Concern and Perception about Home Economics Education Using the Latest Technology in the Era of the 4th Industrial Revolution (4차 산업혁명 시대의 최신 기술을 활용한 가정과교육에 대한 가정과교사의 관심과 인식)

  • Eui Jung Kim;Won Joon Lee;Do Ha Jeong;Sung Mi Cho;Jung Hyun Chae
    • Human Ecology Research
    • /
    • v.61 no.4
    • /
    • pp.673-686
    • /
    • 2023
  • The purpose of this study was to identify home economics (HE) teachers' concerns about and perceptions of HE education using the latest technologies in the era of the 4th Industrial Revolution and to reveal whether they differ according to teachers' general background variables. The questionnaire survey method to measure HE teachers' concerns and perceptions of HE education using the latest technologies in the era of the 4th Industrial Revolution was conducted online using the Google Questionnaire from which 150 responses were received. The main results were as follows. Firstly, HE teachers scored an average of 3.46 out of 5 for the latest technology. Among these interests in the latest technology, interest in "augmented reality and virtual reality technologies" scored the highest at an average of 3.80, while interest in "neural network machine learning" (2.78) was low. HE teacher's concerns about HE education using the latest technologies in the era of the 4th Industrial Revolution were high, with an average score of 4.40. Among these concerns for the latest technology, "concern about the results of HE education using the latest technology" scored the highest at 4.53. HE teachers' anxiety about the latest teaching technology in the era of the 4th Industrial Revolution was moderate, averaging 3.05. The highest form of anxiety was "anxiety about the impact on the job" (4.03) and the lowest was fear of "the disappearance of the teacher's job" (2.50). HE teachers' innovation resistance to the latest teaching technology was low at 2.18. Expectations of the latest technology in HE classes in the era of the 4th Industrial Revolution averaged 3.85, slightly higher than the middle of 3.

Performance Evaluation and Analysis on Single and Multi-Network Virtualization Systems with Virtio and SR-IOV (가상화 시스템에서 Virtio와 SR-IOV 적용에 대한 단일 및 다중 네트워크 성능 평가 및 분석)

  • Jaehak Lee;Jongbeom Lim;Heonchang Yu
    • The Transactions of the Korea Information Processing Society
    • /
    • v.13 no.2
    • /
    • pp.48-59
    • /
    • 2024
  • As functions that support virtualization on their own in hardware are developed, user applications having various workloads are operating efficiently in the virtualization system. SR-IOV is a virtualization support function that takes direct access to PCI devices, thus giving a high I/O performance by minimizing the need for hypervisor or operating system interventions. With SR-IOV, network I/O acceleration can be realized in virtualization systems that have relatively long I/O paths compared to bare-metal systems and frequent context switches between the user area and kernel area. To take performance advantages of SR-IOV, network resource management policies that can derive optimal network performance when SR-IOV is applied to an instance such as a virtual machine(VM) or container are being actively studied.This paper evaluates and analyzes the network performance of SR-IOV implementing I/O acceleration is compared with Virtio in terms of 1) network delay, 2) network throughput, 3) network fairness, 4) performance interference, and 5) multi-network. The contributions of this paper are as follows. First, the network I/O process of Virtio and SR-IOV was clearly explained in the virtualization system, and second, the evaluation results of the network performance of Virtio and SR-IOV were analyzed based on various performance metrics. Third, the system overhead and the possibility of optimization for the SR-IOV network in a virtualization system with high VM density were experimentally confirmed. The experimental results and analysis of the paper are expected to be referenced in the network resource management policy for virtualization systems that operate network-intensive services such as smart factories, connected cars, deep learning inference models, and crowdsourcing.

Necessity to incorporate XR-based Training Contents Focused on Cable pulling using Winches in the Shipbuilding (윈치를 활용한 케이블 포설을 중심으로 고찰한 XR 기반 훈련 콘텐츠 도입의 필요성)

  • JongMin Lee;JongSeong Kim
    • Journal of Korea Society of Industrial Information Systems
    • /
    • v.28 no.6
    • /
    • pp.53-62
    • /
    • 2023
  • This paper has suggested the necessity of introducing training contents using XR(Extended reality) technology as a way to lower the high rate of nursing accidents among unskilled technical personnel in domestic shipbuilding industry, focusing on cable pulling using winch. The occurrence rate of nursing accidents in the domestic shipbuilding industry was almost double(197.4%) (2017~2020) when compared with other manufacturing industries. In particular, it is worth noting that more than 31.8% of nursing accidents in the shipbuilding industry occurred among workers whose job experience is no more than 6 months. Most of new workers are seen to have hard time due to several factors such as lack of work information, inexperience, and unfamiliarity with the working environments. This indicates that it is essential to incorporate more effective training method that could help new workers become familiar with technical skills as well as working environments in a short period of time. Currently, education/training at the domestic shipyard is biased toward technical skills such as welding, painting, machine installation, and electrical installation. Contrary, even more important training required to get new workers used to the working environment has remained at a superficial level such as explaining ship building processes using 2D drawings. This may be the reason why it is inevitable to repeat similar training at OJT (On-the-Job Training) even at the leading domestic companies. Domestic shipbuilding industries have been attracting a lot of new workers thanks to recent economic recovery, which is very likely to increase the occurrence of disasters. In this paper, the introduction of training using XR technology was proposed, and as a specific example, the process of pulling cables using winches on ships was implemented as XR-based training content by using Unity. Using the developed content, it demonstrated that new workers can experience the actual work process in advance through simulation in a virtual space, thereby becoming more effective training content that can help new workers become familiar with the work environment.