• 한국정보전자통신기술학회논문지
• /
• 제13권1호
• /
• pp.88-93
• /
• 2020

최근 SDN 기술이 실제 통신 사업에 적용되면서 사용자가 많아지며 네트워크에 흐르는 데이터 량이 많아짐에 따라 네트워크 데이터 흐름 관리에 대한 관심이 늘고 있다. 이 과정에서 전송되는 네트워크 상의 데이터의 기밀성, 무결성, 가용성, 추적 가능성이 보장되는지 확인할 수 있어야 한다. 또한, 다양한 분야에서 요구되는 네트워크상에서 데이터를 실시간으로 흐름을 관측하고 통제를 시각적으로 확인할 수 있는 환경이 개발이 필요하다. 본 논문에서는 첫 번째로 Mininet을 응용하여 네트워크 토폴로지를 시각적으로 구성하고 다양한 속성을 부여할 수 있는 환경을 구축하였다. 둘째, Mininet 환경에서 OpenDayLight를 추가하여 네트워크 토폴로지에서 네트워크 트래픽 흐름을 시각적으로 확인하고 제어할 수 있는 시뮬레이션 환경을 개발하였다.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2016년도 춘계학술발표대회
• /
• pp.209-210
• /
• 2016

최근 SDN (Software-Defined Networks)에 대한 관심이 증가함에 따라 개인, 학교, 연구소에서 쉽고 간편하게 가상의 네트워크를 구성하고 SDN 기반 네트워크를 테스트를 수행할 수 있는 Mininet이 많이 활용되고 있다. 또한, 여러 SDN 컨트롤러 중에서 ONOS 컨트롤러는 OpenSource로 공개되어 GUI를 이용해 네트워크의 전반적인 토폴로지와 Flow 관리를 쉽게 할 수 있는 성숙된 컨트롤러로 인식되고 있다. 본 논문에서는 Mininet과 ONOS 컨트롤러를 이용하여 SDN 네트워크를 구성하고, 노드가 각 스위치를 이동하여 다닐 때에도 통신이 올바르게 유지되도록 컨트롤하는 시나리오를 구현하고 그 실험 결과를 제시하였다.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2017년도 제55차 동계학술대회논문집 25권1호
• /
• pp.25-26
• /
• 2017

본 논문은 SDN을 가상머신을 통하여 구현하는 방식에 대해 서술한다. SDN은 최근 들어 네트워크 분야의 패러다임으로 부각되었으며 여러 분야에 적용되고 있다. OpenDayLight는 SDN을 구축하기 위한 오픈소스 플랫폼으로 SDN과 NFV를 모두 제공하는 점에 있다. 본 논문에서는 OpenDayLight를 통하여 컨트롤러를 구축하고 Mininet를 통하여 스위치 구축을 통해 SDN 환경을 구축하는 방법에 대하여 서술한다.

• 스마트미디어저널
• /
• 제11권5호
• /
• pp.82-90
• /
• 2022

Along with the growing popularity of 5G technology, providing flexible and personalized network services suitable for requirements of customers has also become a lucrative venture and business key for network service providers. Therefore, dynamic network provisioning is needed to help network service providers. Moreover, increasing user demand for network services meets specific requirements of users, including location, usage duration, and QoS. In this paper, a routing algorithm, which makes routing decisions using Reinforcement Learning (RL) based on the information about link stability, is proposed and called Link Stability aware Reinforcement Learning (LSRL) routing. To evaluate this algorithm, several mininet-based experiments with various network settings were conducted. As a result, it was observed that the proposed method accepts more requests through the evaluation than the past link annotated shorted path algorithm and it was demonstrated that the proposed approach is an appealing solution for dynamic network provisioning routing.

• ETRI Journal
• /
• 제45권3호
• /
• pp.433-447
• /
• 2023

Critical issues such as connection congestion, long transmission delay, and packet loss become even worse during epidemic, disaster, and so on. In this study, a link load balancing method is proposed to address these issues on the data plane, a plane of the software-defined network (SDN) architecture. These problems are NP-complete, so a meta-heuristic approach, discrete particle swarm optimization, is used with a novel hybrid cost function. The superiority of the proposed method over existing methods in the literature is that it provides link and switch load balancing simultaneously. The goal is to choose a path that minimizes the connection load between the source and destination in multipath SDNs. Furthermore, the proposed work is dynamic, so selected paths are regularly updated. Simulation results prove that with the proposed method, streams reach the target with minimum time, no loss, low power consumption, and low memory usage.

• International Journal of Computer Science & Network Security
• /
• 제23권12호
• /
• pp.91-100
• /
• 2023

As SDN devices and systems hit the market, security in SDN must be raised on the agenda. SDN has become an interesting area in both academics and industry. SDN promises many benefits which attract many IT managers and Leading IT companies which motivates them to switch to SDN. Over the last three decades, network attacks becoming more sophisticated and complex to detect. The goal is to study how traffic information can be extracted from an SDN controller and open virtual switches (OVS) using SDN mechanisms. The testbed environment is created using the RYU controller and Mininet. The extracted information is further used to detect these attacks efficiently using a machine learning approach. To use the Machine learning approach, a dataset is required. Currently, a public SDN based dataset is not available. In this paper, SDN based dataset is created which include legitimate and non-legitimate traffic. Classification is divided into two categories: binary and multiclass classification. Traffic has been classified with or without dimension reduction techniques like PCA and LDA. Our approach provides 98.58% of accuracy using a random forest algorithm.

• 한국통신학회논문지
• /
• 제42권4호
• /
• pp.768-779
• /
• 2017

최근 급격히 증가한 모바일 기기의 활성화로 인하여 CMM 기반의 LTE/EPC 네트워크에 과다한 데이터/제어 트래픽의 수용이 힘들어지는 문제가 중요 이슈로 부각되고 있다. 이를 해결하기 위하여 IETF에서는 Distributed Mobility Management (DMM) 기반의 이동성 관리 방안을 제안하였다. 하지만, DMM 기술은 중앙의 트래픽 부하 분산에 초점을 두고 있어서 과다한 제어 트래픽 수용에 관한 문제를 해결하기에는 부족하다. 따라서, 본 논문에서는 이러한 문제를 해결하기 위하여 SDN을 기반으로 CMM과 DMM을 함께 이용하는 C-DMM LTE/EPC 네트워크 구조를 제시한다. 이를 위하여, 기존의 LTE/EPC 네트워크 구조 변경을 최소화 하면서도 효율적인 DMM을 지원하기 위하여 기존 P-GW와 유사한 기능을 수행하는 Packet Data Network Edge Gateway (P-EGW)를 LTE 단말에 근접한 위치에 분산 배치하는 새로운 LTE/EPC 모델을 제안하고, 이를 위하여 단말의 이동성 및 PDN 연결 개수에 따라서 CMM과 DMM 기법 사이 중 하나를 선택하는 방안을 제안한다. 마지막으로, ONOS 컨트롤러와 Mininet 환경에서 각 기법들 사이의 데이터 처리량 및 제어 트래픽의 처리량을 비교하여 제안하는 네트워크 구조 및 기법에 대한 타당성을 입증한다.

• International Journal of Internet, Broadcasting and Communication
• /
• 제12권3호
• /
• pp.9-17
• /
• 2020

In this paper, as part of the research for the infrastructure of very high flexible and reconfigurable data center using very high speed crossbar switches, we developed a simulator that can model two and three dimensional connection structure of switches with an efficient control algorithm using software defined network and verified the functions and analyzed the performance accordingly. The simulator consists of a control module and a switch module that was coded using Python language based on the Mininet and Ryu Openflow frameworks. The control module dynamically controls the operation of switching cells using a shortest multipath algorithm to calculate efficient paths adaptively between configurable computing resources. Performance analysis by using the simulator shows that the three-dimensional switch architecture can accommodate more hosts per port and has about 1.5 times more successful 1:n connections per port with the same number of switches than the two-dimensional architecture. Also simulation results show that connection length in a 3-dimensional way is shorter than that of 2-dimensional way and the unused switch ratio in a 3-dimensional case is lower than that of 2-dimensional cases.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권10호
• /
• pp.4787-4807
• /
• 2016

Although the dense interconnection datacenter networks (DCNs) (e.g. FatTree) provide multiple paths and high bisection bandwidth for each server pair, the single-path TCP (SPT) and ECMP which are widely used currently neither achieve high bandwidth utilization nor have good load balancing. Due to only one available transmission path, SPT cannot make full use of all available bandwidth, while ECMP's random hashing results in many collisions. In this paper, we present OFPT, an OpenFlow based Parallel Transport framework, which integrates precise routing and scheduling for better load balancing and higher network throughput. By adopting OpenFlow based centralized control mechanism, OFPT computes the optimal path and bandwidth provision for each flow according to the global network view. To guarantee high throughput, OFPT dynamically schedules flows with Seamless Flow Migration Mechanism (SFMM), which can avoid packet loss in flow rerouting. Finally, we test OFPT on Mininet and implement it in a real testbed. The experimental results show that the average network throughput in OFPT is up to 97.5% of bisection bandwidth, which is higher than ECMP by 36%. Besides, OFPT decreases the average flow completion time (AFCT) and achieves better scalability.

• 인터넷정보학회논문지
• /
• 제22권5호
• /
• pp.27-33
• /
• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.