• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.93-94
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• 2021

다양한 서비스로부터 발생된 엄청난 양의 데이터는 대량의 네트워크 트래픽을 발생시켜 네트워크 환경의 복잡성을 증대시킨다. 이로 인하여 초고속, 저지연 서비스를 제공하기 위한 방법으로 네트워크 가상화 기술을 도입하였고 그중에서도 SDN 기반의 네트워크 슬라이싱 기법은 네트워크를 논리적으로 분리할 수 있으나 다양한 트래픽을 발생시키는 사용자의 요청에 동적인 대응이 어렵다. 본 논문에서는 LSTM에 사용자 요청의 트래픽 패턴을 학습시켜 네트워크 슬라이스가 자동으로 구성되는 모델을 제안한다.

• Electronics and Telecommunications Trends
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• v.39 no.1
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• pp.36-47
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• 2024

Virtualization technology supports the execution of software unrelated to the hardware environment through the decoupling of software and hardware. Additionally, it enables network slicing, allowing one physical device to be divided and used by a function or service by supporting sharing with isolation. Virtualization enables flexible platform use, allowing a variety of services to be launched without changes or additions to the hardware platform. We describe virtualization technology trends in satellite/mobile communication systems. Basic concepts and technical definitions are included, and the current status of research and development by domestic and foreign organizations, including the Electronics and Telecommunications Research Institute, is analyzed. Finally, future prospects and implications are discussed.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.144-150
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• 2021

The purpose of this paper is to review the direction of the slicing security policy, which is a major consideration in the context of standardization in 5G communication network security, to derive security vulnerability diagnosis items, and to present about analyzing and presenting the issues of discussion for 5G communication network virtualization. As for the research method, the direction of virtualization security policy of 5G communication network of ENISA (European Union Agency for Cybersecurity), a European core security research institute, and research contents such as virtualization security policy and vulnerability analysis of 5G communication network from related journals were used for analysis. In the research result of this paper, the security structure in virtualization security of 5G communication network is arranged, and security threats and risk management factors are derived. In addition, vulnerability diagnosis items were derived for each security service in the risk management area. The contribution of this study is to summarize the security threat items in 5G communication network virtualization security that is still being discussed, to be able to gain insights of the direction of European 5G communication network cybersecurity, and to derive vulnerabilities diagnosis items to be considered for virtualization security of 5G communication network. In addition, the results of this study can be used as basic data to develop vulnerability diagnosis items for virtualization security of domestic 5G communication networks. In the future, it is necessary to study the detailed diagnosis process for the vulnerability diagnosis items of 5G communication network virtualization security.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5701-5722
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• 2018

The continuous increase in the cost of energy production and concerns for environmental sustainability are leading research communities, governments and industries to amass efforts to reduce energy consumption and global $CO_2$ footprint. Players in the information and communication industry are keen on reducing the operational expenditures (OpEx) and maintaining the profitability of cellular networks. Meanwhile, network virtualization has been proposed in this regard as the main enabler for 5G mobile cellular networks. In this paper, we propose a generic framework of slice resource provisioning and customized physical resource allocation for energy-efficiency and quality of service optimization. In resource slicing, we consider user demand and population resources provisioning scheme aiming to satisfy quality of service (QoS). In customized physical resource allocation, we formulate this problem with an integer non-linear programming model, which is solved by a heuristic algorithm based on minimum vertex coverage. The proposed algorithm is compared with the existing approaches, without consideration of slice resource constraints via system-level simulations. From the perspective of infrastructure providers, traffic is scheduled over a limited number of active small-cell base stations (sc-BSs) that significantly reduce the system energy consumption and improve the system's spectral efficiency. From the perspective of virtual network operators and mobile users, the proposed approach can guarantee QoS for mobile users and improve user satisfaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3309-3328
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• 2017

The application of Internet of Things (IoT) in the next generation cellular networks imposes a new characteristic on the data traffic, where a massive number of small packets need to be transmitted. In addition, some emerging IoT-based emergency services require a real-time data delivery within a few milliseconds, referring to as ultra-low latency transmission. However, current techniques cannot provide such a low latency in combination with a mice-flow traffic. In this paper, we propose a dynamic resource reservation schema based on an air-interface slicing scheme in the context of a massive number of sensors with emergency flows. The proposed schema can achieve an air-interface latency of a few milliseconds by means of allowing emergency flows to be transported through a dedicated radio connection with guaranteed network resources. In order to schedule the delay-sensitive flows immediately, dynamic resource updating, silence-probability based collision avoidance, and window-based re-transmission are introduced to combine with the frame-slotted Aloha protocol. To evaluate performance of the proposed schema, a probabilistic model is provided to derive the analytical results, which are compared with the numerical results from Monte-Carlo simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4717-4737
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• 2017

Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.