• Architectural research
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• v.15 no.4
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• pp.191-200
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• 2013

Since Modern Movement flexibility has been one of the most attractive words in architecture. However, "overprovision first, division later" has been the most prevailing design method for spatial flexibility, and many of buildings designed for flexible use are practically quite inflexible due to insufficient building systems or/and irresponsible planning. There have been two dominant strategies to achieve architectural flexibility: multi-functionality and polyvalence. These two approaches, which point contradictory directions, actually reflect the difficulty in providing a proper form of architectural flexibility. Multi-functionality can afford changeable environments with satisfying spatial conditions; however it lacks tolerance to accommodate other uses but intended functions by architects. Meanwhile, flexibility by a polyvalent form relies on the vague anticipation of user's various interpretations. In this study by looking up these two different standpoints and historical precedents flexibility in architecture is carefully scrutinized focused on the contradiction, and as an alternative for architectural flexibility contextual relations is proposed. Unlike both multi-functionality and polyvalence, which produce flexibility by changing its own properties, manipulating contextual relations infuses flexibility into space by changing the properties of a building, not of its individual room. By using this contextual relations method, a community-centered school in Manhattan, NY, which was in danger of being closed because of its academic failure, is represented as a flexible space.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.343-348
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• 2022

Recently, With the recent rapid development of technology, the amount of data generated by various systems is increasing, and enterprise servers and data centers that have to handle large amounts of big data need to apply high-stability and high-performance storage devices even if costs increase. In such systems, SSD(solid state disk) that provide high performance of read/write are often used as storage devices. However, due to the characteristics of reading and writing on a page-by-page basis, erasing operations on a block basis, and erassing-before-writing, there is a problem that performance is degraded when duplicate writes occur. Therefore, in order to delay this performance degradation problem, over-provision technology of SSD has been applied internally. However, since over-provided technologies have the disadvantage of consuming a lot of storage space instead of performance, the application of inefficient technologies above the right performance has a problem of over-costing. In this paper, we proposed a method of measuring the performance and cost incurred when various over-provisions are applied in an SSD and predicting the system-optimized over-provided ratio based on this. Through this research, we expect to find a trade-off with costs to meet the performance requirements in systems that process big data.

• 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.