• ETRI Journal
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• v.42 no.2
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• pp.186-195
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• 2020

Software-defined networking (SDN) acts as a centralized management unit, especially in a network with devices that operate under the transport layer of the OSI model. However, when a network with layer 7 middleboxes (MBs) is considered, current SDNs exhibit limitations. As such, to achieve a real-centralized management unit, a new architecture is required that decouples the data and control planes of all network devices. In this report, we propose such a complementary architecture to the current SDN in which SDN-enabled MBs are included along with contemporary SDN-enabled switches. The management unit of this architecture improves network performance and reduces routing cost by considering the status of the MBs during flow forwarding. This unit consists of the following two parts: an SDN controller (SDNC) and a middlebox controller (MBC). The latter selects the best MBs for each flow and the former determines the best path according to its routing algorithm and provides information via the MBC. The results show that the proposed architecture improved performance because the utilization of all network devices including MBs is manageable.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.2
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• pp.215-221
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• 2016

Data failure in wireless communications considerably affects the reconstruction quality of transmitted data. Traditionally, fascinating trials have been conducted to overcome the data failure intensifying reliable reconstruction of a media. But, none of these efforts neither effective, computationally inexpensive nor simply configurable to reduce the problems of transmitting media or images. In practice, it is necessary to maintain the quality of transmitted image without sacrificing any data, content, or information. So, to deal with dynamic events such as sensor node participation and departure, during transmission, an efficient scheme is important. For this reason, a new robust scheme has been presented in this paper to minimize the limitation of traditional wireless networking. This scheme uses Software-Defined Image Sensor Network (SD-ISN) to ensure scalability and dependability of the sensor network of handling data losses. Finally, a comparison of our proposed SD-ISN with conventional wireless networking has been presented in simulation to test the robustness and effectiveness of our proposed SD-ISN approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.514-535
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• 2019

Information-centric networks operate under the assumption that all network components have built-in caching capabilities. Integrating the caching strategies of information centric networking (ICN) with wireless virtualization improves the gain of virtual infrastructure content caching. In this paper, we propose a framework for software-defined information centric virtualized wireless device-to-device (D2D) networks. Enabling D2D communications in virtualized ICN increases the spectral efficiency due to reuse and proximity gains while the software-defined network (SDN) as a platform also simplifies the computational overhead. In this framework, we propose a joint virtual resource and cache allocation solution for latency-sensitive applications in the next-generation cellular networks. As the formulated problem is NP-hard, we design low-complexity heuristic algorithms which are intuitive and efficient. In our proposed framework, different services can share a pool of infrastructure items. We evaluate our proposed framework and algorithm through extensive simulations. The results demonstrate significant improvements in terms of visiting latency, end user QoE, InP resource utilization and MVNO utility gain.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.374-386
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• 2022

Nowadays, research in deep learning leveraged automated computing and networking paradigm evidenced rapid contributions in terms of Software Defined Networking (SDN) and its diverse security applications while handling cybercrimes. SDN plays a vital role in sniffing information related to network usage in large-scale data centers that simultaneously support an improved algorithm design for automated detection of network intrusions. Despite its security protocols, SDN is considered contradictory towards DDoS attacks (Distributed Denial of Service). Several research studies developed machine learning-based network intrusion detection systems addressing detection and mitigation of DDoS attacks in SDN-based networks due to dynamic changes in various features and behavioral patterns. Addressing this problem, this research study focuses on effectively designing a multistage hybrid and intelligent deep learning classifier based on modified deep forest classification to detect DDoS attacks in SDN networks. Experimental results depict that the performance accuracy of the proposed classifier is improved when evaluated with standard parameters.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.145-146
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• 2019

SDN(Software Defined Networking) 환경에서는 OpenFlow 프로토콜을 사용함으로써, 컨트롤러는 스위치가 패킷의 도착이나 Table의 상태에 따라 미리 Flow table의 Entry를 추가, 갱신, 삭제하도록 제어한다. 본 논문에서는 Flow entry의 사용량에 대한 확률을 정확하게 측정하기 위하여 Hidden Markov Mode (HMM)을 적용한 새로운 Flow entry 사전 제거 기법을 제안한다. 본 연구를 통해 HMM을 사용하여 기존 기술들보다 효과적이며 Flow table 관리에 있어 향상된 성능을 목표로 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.147-148
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• 2019

네트워크 응용 서비스들은 점점 더 복잡해지고 있으며, 네트워크 통신 기술의 발전과 함께 네트워크의 특성, 네트워크 관리 및 혼잡 제어에 대한 높은 요구 사항을 제시하므로 네트워크 트래픽 분류가 점점 더 중요해지고 있다. 트래픽 분류는 다양한 특성에 따라 네트워크 트래픽을 여러 클래스로 분류하여 처리하는 작업이다. 본 논문에서는 현재 네트워크 분야에서 적용된 여러 트래픽 분류 기법을 조사한다. 이를 통해 SDN(Software Defined Networking) 환경에서 효율적인 트래픽 분류가 가능한 기법 선택을 위해 비교하며 향후 연구를 위해 트래픽 분류 기법들을 소개한다.

• Information and Communications Magazine
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• v.30 no.3
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• pp.67-72
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• 2013

본 고에서는, 미래 인터넷 (Future Internet) 연구에 있어서, 가장 많은 관심과 가능성 예측에 기반한 실제적인 투자를 이끌어 내고 있는 두 개의 주요 핵심 기술 분야인 콘텐츠 중심 네트워킹 (Content-Centric Networking: CCN)과 소프트웨어 정의 네트워킹 기술 (Software-Defined Networking: SDN)의 접목에 대해서, 그 배경과 당위성 그리고 관련 연구 동향을 살펴 본다.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.1-12
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• 2015

To realize futuristic services with agility, the role of the experimental facility (i.e., testbed) based on integrated resources has become important, so that developers can flexibly utilize the dynamic provisioning power of software-defined networking and cloud computing. Following this trend, an OpenFlow-based SDN testbed environment, denoted as OF@TEIN, connects multiple sites with unique SmartX Racks (i.e., virtualization-enabled converged resources). In this paper, in order to automate the multi-point L2 (i.e., Ethernet) inter-connection of OpenFlow islands, we introduce an automated tool to configure the required Network Virtualization using Generic Routing Encapsulation (NVGRE) tunneling. With the proposed automation tool, the operators can efficiently and quickly manage network inter-connections among multiple OpenFlow sites, while letting developers to control their own traffic flows for service realization experiments.

• KNOM Review
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• v.21 no.2
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• pp.35-45
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• 2018

As Network Slicing functionality gets applied to mobile networking, a mechanism that enables the selection of network slices becomes indispensable. Following the 3GPP Technical Specification for the 5G Architecture, the inclusion of the Network Slice Selection Function (NSSF) in order to leverage the process of slice selection is apparent. However, actual implementation of this network function needs to deal with the dynamic changes of network instances, due to this, a platform that supports the orchestration of Virtual Network Functions (VNF) is required. Our proposed solution include the use of the Central Office Rearchitected as a Data Center (CORD) platform, with the specified profile for mobile networks (M-CORD) that integrates a service orchestrator (XOS) alongside solutions oriented to Software Defined Networking (SDN), Network Function Virtualization (VNF) and virtual machine management through OpenStack, in order to provide the right ecosystem where our implementation of NSSF can obtain slice information dynamically by relying on synchronization between back-end services and network function instances.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.1-7
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• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.