• Journal of Information Processing Systems
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• v.13 no.2
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• pp.236-255
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• 2017

The rapid growth of smart devices demands an enhanced throughput for network connection sustainability during mobility. However, traditional wireless network architecture suffers from mobility management issues. In order to resolve the traditional mobility management issues, we propose a novel architecture for future wireless access network based on software-defined network (SDN) by using the advantage of network function virtualization (NFV). In this paper, network selection approach (NSA) has been introduced for mobility management that comprises of acquiring the information of the underlying networking devices through the OpenFlow controller, percepts the current network behavior and later the selection of an appropriate action or network. Furthermore, mobility-related scenarios and use cases to analyze the implementation aspects of the proposed architecture are provided. The simulation results confirm that the proposed scenarios have obtained a seamless mobility with enhanced throughput at minimum packet loss as compared to the existing IEEE 802.11 wireless network.

• ETRI Journal
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• v.40 no.1
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• pp.72-88
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• 2018

In the fifth generation (5G) era, the radio internet protocol capacity is expected to reach 20 Gb/s per sector, and ultralarge content traffic will travel across a faster wireless/wireline access network and packet core network. Moreover, the massive and mission-critical Internet of Things is the main differentiator of 5G services. These types of real-time and large-bandwidth-consuming services require a radio latency of less than 1 ms and an end-to-end latency of less than a few milliseconds. By distributing 5G core nodes closer to cell sites, the backhaul traffic volume and latency can be significantly reduced by having mobile devices download content immediately from a closer content server. In this paper, we propose a novel solution based on software-defined network and network function virtualization technologies in order to achieve agile management of 5G core network functionalities with a proof-of-concept implementation targeted for the PyeongChang Winter Olympics and describe the results of interoperability testing experiences between two core networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.142-146
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• 2014

The mobile computing has been a main mean of computing, because of recent development of smartphone and mobile communication network. The new service technologies, such as cloud computing, big data, SDN (Software Defined Network), and NFV (Network Function Virtualization), are appearing due to activating of the data center. These changes have brought various requirements for transport layer protocol. In addition, the transport protocol should have a extensible function to adapt additional technologies. This paper proposed that transport layer divide into three layer structure for extensible function, and designed the ETP(Extensible Transport Protocol) which is located in the bottom layer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3584-3602
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• 2022

NFV realizes flexible and rapid software deployment and management of network functions in the cloud network, and provides network services in the form of chained virtual network functions (VNFs). However, using VNFs to provide quality guaranteed services is still a challenge because of the inherent difficulty in intelligently scaling VNFs to handle traffic fluctuations. Most existing works scale VNFs with fixed-capacity instances, that is they take instances of the same size and determine a suitable deployment location without considering the cloud network resource distribution. This paper proposes a traffic forecasted assisted proactive VNF scaling approach, and it adopts the instance capacity adaptive to the node resource. We first model the VNF scaling as integer quadratic programming and then propose a proactive adaptive VNF scaling (PAVS) approach. The approach employs an efficient traffic forecasting method based on LSTM to predict the upcoming traffic demands. With the obtained traffic demands, we design a resource-aware new VNF instance deployment algorithm to scale out under-provisioning VNFs and a redundant VNF instance management mechanism to scale in over-provisioning VNFs. Trace-driven simulation demonstrates that our proposed approach can respond to traffic fluctuation in advance and reduce the total cost significantly.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• Electronics and Telecommunications Trends
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• v.32 no.1
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• pp.25-34
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• 2017

무선통신, 이동통신 및 사물인터넷 기술의 발달에 힘입어 인터넷의 규모와 복잡도는 해마다 증가하고 있으며, 망의 제어와 관리의 복잡도 역시 함께 증가할 것으로 예상된다. 이에 따라 운용자(operator)가 담당하던 제어와 관리를 망이 스스로 수행하는 자율네트워킹(autonomic networking) 기술이 등장하게 되었다. 초기의 자율네트워킹 연구는 자가관리(self-management)를 위한 프레임워크를 개발하는 것에 중점을 두었으나, 이후에는 SDN/NFV 기반 플랫폼에 기계학습 기술을 접목함으로써, 유연성이 확보된 망에 지능화된 제어 및 관리를 제공하는 방향으로 진화하고 있다. 본고에서는 자율네트워킹에 관한 최근의 연구동향을 소개한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.322-323
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• 2017

본 논문은 대규모 트래픽을 처리하는 NFV환경의 서비스 기능 체이닝 영역에서 sFlow 기반의 트래픽 분류기를 이용한 트래픽 관리 기법을 제안하고 있다. sFlow의 실시간 트래픽 샘플링을 사용하여 실시간으로 변화하는 트래픽의 효과적인 관리를 기대할 수 있으며, 제안한 트래픽 관리 기법은 대규모 트래픽의 네트워크 안정성과 보안을 향상시킨다.

• Information and Communications Magazine
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• v.31 no.9
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• pp.46-51
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• 2014

미래지향적 네트워크 및 서비스 인프라의 구축을 위해 네트워크의 개방화와 가상화에 대한 관심이 높아졌다. 이를 지원하는 기술로서 SDN (Software-defined Networking) 및 NFV(Network Function Virtualisation) 기술이 소개되었다. 특히 트래픽에 따라 필요한 네트워크 기능들을 선택적으로 조합 및 실행하여 하나의 네트워크 서비스를 구현하는 서비스 체이닝(Service Chaining 혹은 Service Function Chaining) 기술이 높은 관심을 받고 있다. 이를 통해 컴포넌트 서비스들로 이루어진 경로를 정의함으로써 네트워크 서비스를 적시에 구성 및 능동적으로 제어할 수 있다. 본 고에서는 서비스 체이닝 기술의 기본 개념및 기능에 대해 간략히 소개하고, 주요 기능의 표준화를 담당하는 IETF SFC WG 의 주요 표준화 이슈에 대한 분석 및 향후 전망을 기술한다.

• Information and Communications Magazine
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• v.31 no.9
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• pp.3-8
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• 2014

본고에서는 분산형 이동성 관리 기법(Distributed Mobility Management, DMM)에 대한 표준화 연구 동향 및 이를 통한 모바일 네트워크에서의 적용 방안에 대한 연구 동향을 소개한다. 특히, 국제 표준화 단체인 IETF에서 논의되고 있는 분산형 이동성 관리 기법의 방향과 함께 최근 활발하게 논의되는 소프트웨어 정의 네트워크(Software-Defined Networking, SDN) 및 네트워크 기능 가상화(Network Function Virtualization, NFV)기술과의 접목을 통한 연구들을 소개하고 다양한 기술의 접목에 따른 이슈들을 분석한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.114-116
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• 2016

Recently, SDN and NFV technology have been developed actively and provide enormous flexibility of network provisioning. The future network services would generally involve many different types of services such as hologram games, social network live streaming videos and cloud-computing services, which have dynamic service requirements. To provision networks for future services dynamically and efficiently, SDN/NFV orchestrators must clearly understand the service requirements. Currently, network provisioning relies heavily on QoS parameters such as bandwidth, delay, jitter and throughput, and those parameters are necessary to describe the network requirements of a service. However it is often difficult for users to understand and use them proficiently. Therefore, in order to maintain interoperability and homogeneity, it is required to have a service abstraction layer between users and orchestrators. The service abstraction layer analyzes ambiguous user's requirements for the desired services, and this layer generates corresponding refined services requirements. In this paper, we present our initial effort to design a Smart Service Abstraction Layer (SmSAL) for future network architecture, which takes advantage of machine learning method to analyze ambiguous and abstracted user-friendly input parameters and generate corresponding network parameters of the desired service for better network provisioning. As an initial proof-of-concept implementation for providing viability of the proposed idea, we implemented SmSAL with a decision tree model created by learning process with previous service requests in order to generate network parameters related to various audio and video services, and showed that the parameters are generated successfully.