• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.95-98
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• 2021

최근 비정상적인 네트워크 활동 감지 및 네트워크 서비스 프로비저닝과 같은 다양한 분야에서 응용되는 네트워크 트래픽 예측 기술이 네트워크 통신 문제에 의한 트래픽의 결측 및 네트워크 유저의 불규칙한 활동에 의한 비선형 특성 때문에 발생하는 성능 저하를 극복하기 위해 딥러닝 신경망에 대한 연구가 활성화되고 있다. 이 딥러닝 신경망 중 시계열 딥러닝 신경망은 단기 네트워크 트래픽 볼륨을 예측할 때 낮은 오류율을 보인다. 하지만, 시계열 딥러닝 신경망은 기울기 소멸 및 폭발과 같은 비선형성, 다중 계절성 및 장기적 의존성 문제와 같은 한계를 보여준다. 이 논문에서는 계절성 임베딩을 고려한 주의 신경망 기반 트래픽 예측 기법을 제안한다. 제안하는 기법은 STL 분해 기법을 통해 분해된 트래픽 트랜드, 계절성, 잔차를 이용하여 일별 및 주별 계절성을 임베딩하고 이를 주의 신경망을 기반으로 향후 트래픽을 예측한다.

• 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.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.568-574
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• 2013

In recent years, IT companies offer various cloud services using hardware-based technologies or software-based technologies. User can access these cloud services without the constraints of location or devices. The technologies are virtualization, provisioning, and big data processing. However, security incidents are constantly occurring even with these techniques. Thus, many companies build and operate private cloud service to prevent the leak of critical data. If virtual environment are different according to user permission, many system are needed, and user should login several virtual system to execute an program. In this paper, I suggest the access control method for application software on virtual operating system using the Open Authentication protocol in the Cloud system.

• KNOM Review
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• v.22 no.1
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• pp.1-10
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• 2019

The process to determine the required number of resources depends on the factors being considered. Autoscaling is one such mechanism that uses a wide range of factors to decide and is a critical process in NFV. As the networks are being shifted onto the cloud after the invention of SDN, we require better resource managers in the future. To solve this problem, we propose a solution that allows the VNFMs to autoscale the system resources depending on the factors such as overhead of hyperthreading, number of requests, execution-times for the virtual network functions. It is a known fact that the hyperthreaded virtual-cores are not fully capable of performing like the physical cores. Also, as there are different types of core having different frequencies so the process to calculate the number of cores needs to be measured accurately and precisely. The platform independency is achieved by proposing another solution in the form of a monitoring microservice, which communicates through APIs. Hence, by the use of our autoscaling application and a monitoring microservice, we enhance the resource provisioning process to meet the criteria of future networks.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.1-10
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• 2014

The cloud computing paradigm introduced pay-per-use models in which IT services can be created and scaled on-demand. However, service providers are still concerned about the constraints imposed by their physical infrastructures. In order to keep the required QoS and achieve the goal of upholding the SLA, virtualized resources must be efficiently consolidated to maximize system throughput while keeping energy consumption at a minimum. Using ANN, we propose a predictive SLA-aware approach for consolidating virtualized resources in a cloud environment. To maintain the QoS and to establish an optimal trade-off between performance and energy efficiency, the server's utilization threshold dynamically adapts to the physical machine's resource consumption. Furthermore, resource-intensive VMs are prevented from getting underprovisioned by assigning them to hosts that are both capable and reputable. To verify the performance of our proposed approach, we compare it with non-optimized conventional approaches as well as with other previously proposed techniques in a heterogeneous cloud environment setup.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.250-255
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• 2020

For the resolution of the latency problem of the cloud center-based cloud computing, fog computing was proposed that allows end devices to offload computations to nearby fog nodes. In the fog computing, virtualized service images are placed on fog nodes and, if service images are placed close to end devices, the duplicate service image placement problem may occur. Therefore, in this paper, we propose a service image placement mechanism based on the logical fog network that reduces duplicate service images by considering the pattern of collected service requests. For the performance evaluation of the proposed mechanism, through simulations, we compare ours with the on-demand mechanism placing a service image upon the receipt of a service request. We consider the performance factors like the number of service images, the number of non-accommodated service requests, and the network cost.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.491-500
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• 2006

Since users want to have services correctly in their own position and surrounding circumstance in ubiquitous computing environment, it is very important to search, compose basic services and provide suitable services according to various circumstances. However existing techniques have been studied on service discovery mainly and lack consideration for position or preference of users. Furthermore, on service composition, they lists basic services simply and do not propose concretely method of use service history data for service composition. Therefore we propose a framework for context-based service provisioning middleware system, called COSEP, and Ontology engine with data ming. This research discovers services by reacting dynamically to circumstance information such as time and position of user, composites services using Ontology engine with data ming and offers newly created optimal services to users.