• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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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.

• 전자통신동향분석
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• 제39권1호
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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.

• 융합정보논문지
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• 제11권10호
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• pp.144-150
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• 2021

본 논문의 연구목적은 5G 통신네트워크 보안에서 표준화가 진행되고 있는 상황에서 주요 고려 사항인 슬라이싱 보안 정책에 대한 방향을 검토하고, 5G 통신 네트워크 가상화의 보안 취약점 진단 항목들을 도출하며, 위험관리에 대한 주요 논의 사항들을 분석하고 제시하는데 있다. 연구방법은 유럽 핵심보안 연구기관인 ENISA(European Union Agency for Cybersecurity)의 5G 통신네트워크의 가상화 보안 정책 방향과, 국외 주요 관련 저널로부터 5G 통신네트워크의 가상화 보안정책과 취약점 분석 등의 연구 내용을 분석에 활용하였다. 본 논문의 연구 결과에서는 5G 통신 네트워크의 가상화 보안에서 보안구조를 정리하였고, 보안 위협들과 위험관리 요소를 도출하였다. 또한 위험관리 영역에서 보안 서비스별로 취약점 진단 항목들을 도출하였다. 본 연구의 기여도는 여전히 논의 되고 있는 5G 통신 네트워크 가상화 보안에서 보안 위협 항목들을 요약하였다는 것과, 유럽의 5G 통신네트워크 사이버보안 방향을 파악 할 수 있었다는 것, 그리고 5G 통신 네트워크의 가상화 보안에 고려되어야 하는 취약점 진단 항목들을 도출하였다는 데 있다. 아울러 본 연구의 결과는 국내 5G 통신네트워크 가상화 보안을 위한 취약점 진단 항목들을 개발하는데 기초 자료로 활용 될 수 있다. 향후 5G 통신네트워크 가상화 보안의 취약점 진단 항목에 대한 상세한 진단 프로세스를 연구하는 것이 필요하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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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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• 제11권7호
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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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• 제11권10호
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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.