• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.91-101
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• 2024

The utilization of container virtualization technology ensures the consistency and portability of data-intensive and memory volatile workflows. Kubernetes serves as the de facto standard for orchestrating these container applications. Cloud users often overprovision container applications to avoid container restarts caused by resource shortages. However, overprovisioning results in decreased CPU and memory resource utilization. To address this issue, oversubscription of container resources is commonly employed, although excessive oversubscription of memory resources can lead to a cascade of container restarts due to node memory scarcity. Container restarts can reset operations and impose substantial overhead on containers with high memory volatility that include numerous stateful applications. This paper proposes a technique to mitigate container restarts in a memory oversubscription environment based on Kubernetes. The proposed technique involves identifying containers that are likely to request memory allocation on nodes experiencing high memory usage and temporarily pausing these containers. By significantly reducing the CPU usage of containers, an effect similar to a paused state is achieved. The suspension of the identified containers is released once it is determined that the corresponding node's memory usage has been reduced. The average number of container restarts was reduced by an average of 40% and a maximum of 58% when executing a high memory volatile workflow in a Kubernetes environment with the proposed method compared to its absence. Furthermore, the total execution time of a container workflow is decreased by an average of 7% and a maximum of 13% due to the reduced frequency of container restarts.

• Journal of KIBIM
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• v.13 no.4
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• pp.74-84
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• 2023

Modern construction projects have stakeholders from various construction fields, highlighting the importance of efficient information sharing and collaboration. The expanding scope of Building Information Modeling (BIM), particularly in the domestic construction sector, necessitates a Common Data Environment (CDE). However, applying foreign commercial CDE solutions within the domestic context is challenging due to the difficulty of aligning them with the unique organizational structures and characteristics prevalent in the country. Furthermore, the information review and approval processes specified by ISO 19650 often fail to harmonize adequately with the domestic design procedures, limiting the full utilization of CDE advantages. This study endeavors to develop a Korean CDE collaborative platform based on ISO 19650, with a focus on adapting workflows and information container systems to the domestic context. Building upon the requirements of ISO 19650-based CDE workflows and information containers, this research involves an in-depth analysis of information generation, sharing, review, and approval processes within domestic design organizations, offering tailored CDE workflows and information container systems that align with the specific needs of the Korean construction industry.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.191-203
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• 2013

Today, information technologies have been applied to operations in container terminals, and it is possible to collect operational log data due to development of equipment and operations technology. Terminal operators are collecting event log data and try to figure out the way of resolving operations problems. Operators want to analyze event logs to determine the causes of the operation problems, but it can hardly be done manually. In this paper, we suggest a formal framework to evaluate performance measures using the collected log data of operations in container terminals. The proposed formal framework supports different container terminal layout, operational processes, and equipment. Our formal framework is composed of specification of terminal layout, log data, workflow, statistics, and report, based on the concept of container handling objects. For validation of our framework, we have implemented a terminal performance analysis system based on the proposed framework.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.587-595
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• 2015

Several fields of science have traditionally demanded large-scale workflow support, which requires thousands of central processing unit (CPU) cores. In order to support such large-scale scientific workflows, large-capacity cluster systems such as supercomputers are widely used. However, as users require a diversity of software packages and configurations, a system administrator has some trouble in making a service environment in real time. In this paper, we present a container-based cluster management platform and introduce an implementation case to minimize performance reduction and dynamically provide a distributed computing environment desired by users. This paper offers the following contributions. First, a container-based virtualization technology is assimilated with a resource and job management system to expand applicability to support large-scale scientific workflows. Second, an implementation case in which docker and HTCondor are interlocked is introduced. Lastly, docker and native performance comparison results using two widely known benchmark tools and Monte-Carlo simulation implemented using various programming languages are presented.

• Journal of KIISE
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• v.44 no.5
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• pp.439-448
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• 2017

The workloads of large high-performance computing (HPC) scientific applications are steadily becoming "bursty" due to variable resource demands throughout their execution life-cycles. However, the over-provisioning of virtual resources for optimal performance during execution remains a key challenge in the scheduling of scientific HPC applications. While over-provisioning of virtual resources guarantees peak performance of scientific application in virtualized environments, it results in increased amounts of idle resources that are unavailable for use by other applications. Herein, we proposed a memory resource reconfiguration approach that allows the quick release of idle memory resources for new applications in OS-level virtualized systems, based on the applications resource-usage pattern profile data. We deployed a scientific workflow application in Docker, a light-weight OS-level virtualized system. In the proposed approach, memory allocation is fine-tuned to containers at each stage of the workflows execution life-cycle. Thus, overall memory resource utilization is improved.