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VNF Anomaly Detection Method based on Unsupervised Machine Learning
Seondong Heo, Seunghoon Jeong, Hosang Yun
http://doi.org/10.5626/JOK.2022.49.9.780
By applying virtualization technology to telecommunication networks, it is possible to reduce hardware dependencies and provide flexible control and management to the operators. In addition, since Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) can be reduced by utilizing the technology, modern telco operators and service providers are using Software-Defined Networking(SDN) and Network Function Virtualization (NFV) technology to provide services more efficiently. As SDN and NFV are widely used, cyber attacks on Vitualized Network Functions (VNF) that degrade the quality of service or cause service denial are increasing. In this study, we propose a VNF anomaly detection method based on unsupervised machine learning techniques that models the steady states of VNFs and detects abnormal states caused by cyber attacks.
Learning-based QoS Path Prediction Method in SDN Environment
Seunghoon Jeong, Seondong Heo, Hosang Yun
http://doi.org/10.5626/JOK.2021.48.11.1241
When Quality of Service (QoS) is supported by flow path control in Software-Defined Networking (SDN) environment, the current simple least cost path finding method can cause inefficient rerouting problems. The measured performance of the flow path derived based on the link quality may differ from the predicted performance. In particular, in the case of sequential QoS condition search for candidate paths, the effectiveness of path-based QoS support may decrease due to repeatedly searching for the same path previously identified as the final path. In this paper, we propose a learning-based QoS path search model. The model learns the path that finally satisfies the QoS conditions according to the network state, and predicts the QoS path for the network state when rerouting is required. The experiment shows that this learning model can reduce unnecessary path iteration search costs given the similar network conditions, and is more effective than other learning-based models in a service environment that requires rapid QoS quality restoration.
Traffic Steering System with Dual Connectivity for Video Streaming Services
Gi Seok Park, Hyunmin Noh, Jae Jun Ha, Hyung Jun Kim, Sang Heon Shin, Dong Hyun Kim, Jong Hwan Ko, Jeung Won Choi, Hwangjun Song
http://doi.org/10.5626/JOK.2019.46.6.577
In this paper, we propose a traffic steering system with dual connectivity to provide stable video streaming services for users by steering portion of the macrocell traffic into small cells. The proposed system achieves a good balance between fairness and social welfare in terms of video quality by allocating the radio resource of the macro base station. The user data flow is divided into two channels toward the macro base station and the small cell AP, and the users receive their data from both. In the proposed system, the fountain code is adopted to overcome practical issues in the dual connectivity. Moreover, the SDN is employed not only to rapidly react to time-varying network condition, but also to control network resources efficiently. The proposed system is implemented using NS-3. The simulation results show that the proposed system can achieve much better performance compared with existing traffic steering algorithms.
LTRE: Lightweight Traffic Redundancy Elimination in Software-Defined Wireless Mesh Networks
Gwangwoo Park, Wontae Kim, Joonwoo Kim, Sangheon Pack
http://doi.org/10.5626/JOK.2017.44.9.976
Wireless mesh network (WMN) is a promising technology for building a cost-effective and easily-deployed wireless networking infrastructure. To efficiently utilize limited radio resources in WMNs, packet transmissions (particularly, redundant packet transmissions) should be carefully managed. We therefore propose a lightweight traffic redundancy elimination (LTRE) scheme to reduce redundant packet transmissions in software-defined wireless mesh networks (SD-WMNs). In LTRE, the controller determines the optimal path of each packet to maximize the amount of traffic reduction. In addition, LTRE employs three novel techniques: 1) machine learning (ML)-based information request, 2) ID-based source routing, and 3) popularity-aware cache update. Simulation results show that LTRE can significantly reduce the traffic overhead by 18.34% to 48.89%.
An Interference Reduction Scheme Using AP Aggregation and Transmit Power Control on OpenFlow-based WLAN
Mi-Rim Do, Sang-Hwa Chung, Chang-Woo Ahn
Recently, excessive installations of APs have caused WLAN interference, and many techniques have been suggested to solve this problem. The AP aggregation technique serves to reduce active APs by moving station connections to a certain AP. Since this technique forcibly moves station connections, the transmission performance of some stations may deteriorate. The AP transmit power control technique may cause station disconnection or deterioration of transmission performance when power is reduced under a certain level. The combination of these two techniques can reduce interference through AP aggregation and narrow the range of interferences further through detailed power adjustment. However, simply combining these techniques may decrease the probability of power adjustment after aggregation and increase station disconnections upon power control. As a result, improvement in performance may be insignificant. Hence, this study suggests a scheme to combine the AP aggregation and the AP transmit power control techniques in OpenFlow-based WLAN to ameliorate the disadvantages of each technique and to reduce interferences efficiently by performing aggregation for the purpose of increasing the probability of adjusting transmission power. Simulations reveal that the average transmission delay of the suggested scheme is reduced by as much as 12.8% compared to the aggregation scheme and by as much as 18.1% compared to the power control scheme. The packet loss rate due to interference is reduced by as much as 24.9% compared to the aggregation scheme and by as much as 46.7% compared to the power control scheme. In addition, the aggregation scheme and the power control scheme decrease the throughput of several stations as a side effect, but our scheme increases the total data throughput without decreasing the throughput of each station.
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