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Real-time Monitoring of Packet Processing Time for Virtual Network Functions
James Won-Ki Hong,Jae-Hyoung Yoo,Nguyen Van Tu 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
By enabling the deployment of softwarelized network functions on commodity servers, Network Function Virtualization (NFV) brings many benefits such as rapid development and deployment, simplicity and flexibility in network operations and management. Monitoring the performance characteristics of Virtual Network Functions (VNFs), such as packet processing time, is crucial to achieving maximum benefit from NFV. In this paper, we present Packer Processing Time Monitoring (PPTMon) - a solution for real-time and lightweight VNF packet processing time monitoring. PPTMon embeds timestamp information directly into the packets. PPTMon is implemented using extended Berkeley Packet Filter (eBPF) - a new Linux framework that allows high-speed packet processing. Our experiments showed that PPTMon can monitor VNFs with high accuracy and low performance overhead.
Q-Learning based SFC deployment on Edge Computing Environment
Suman Pandey,James Won-Ki Hong,Jae-Hyoung Yoo 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
Reinforcement learning (RL) has been used in various path finding applications including games, robotics and autonomous systems. Deploying Service Function Chain (SFC) with optimal path and resource utilization in edge computing environment is an important and challenging problem to solve in Software Defined Network (SDN) paradigm. In this paper we used RL based Q-Learning algorithm to find an optimal SFC deployment path in edge computing environment with limited computing and storage resources. To achieve this, our deployment scenario uses a hierarchical network structure with local, neighbor and datacenter servers. Our Q-Learning algorithm uses an intuitive reward function which does not only depend on the optimal path but also considers edge computing resource utilization and SFC length. We defined regret and empirical standard deviation as evaluation parameters. We evaluated our results by making 1200 test cases with varying SFC-length, edge resources and Virtual Network Function’s (VNF) resource demand. The computation time of our algorithm varies between 0.03~0.6 seconds depending on the SFC length and resource requirement.
Kim, Woojoong,Hong, James Won-Ki,Suh, Young-Joo IEEE 2019 IEEE COMMUNICATIONS LETTERS Vol.23 No.2
<P>The existing elastic control planes (ECPs) suffer from the immediacy and the computing overhead issues in software-defined data center networks (SD-DCNs). In this letter, we propose T-DCORAL which is a new ECP to mitigate both issues in SD-DCNs. T-DCORAL accelerates/decelerates the control plane by allocating a virtual CPU to controllers in runtime, whereas the existing ECPs resize the controller pool. As a result, T-DCORAL maximally reduces the latency to adjust the control plane from 46 s to 38 ms, the average CPU load by 22%, and the average rule installation time by 64.28%.</P>
머신러닝을 이용한 VNF 이상 탐지 및 고장 예측 기반 NFV 관리 시스템 설계
홍지범(Jibum Hong),정세연(Seyeon Jeong),남석현(Sukhyun Nam),유재형(Jae-Hyoung Yoo),홍원기(James Won-Ki Hong) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
5G 네트워크는 소프트웨어 정의 네트워킹 (Software-Defined Networking, SDN)과 네트워크 기능 가상화 (Network Function Virtualization, NFV) 기술을 이용해 가상화된 네트워크 환경을 구축하고 사용자의 서비스 요청을 보다 유연하고 민첩하게 처리하고 있다. 하지만 가상 네트워크가 점점 복잡해짐에 따라 다양한 관리 문제가 발생하고 있으며, 이러한 관리 문제를 해결하기 위해서는 가상 네트워크 환경을 구성하는 서버 및 VNF (Virtualized Network Function)들을 모니터링하여 최적의 관리 정책을 도출할 필요성이 증가하고 있다. 본 논문에서는 가상 네트워크 모니터링 및 분석과 함께 머신러닝을 이용해 VNF 의 이상 탐지 (anomaly detection) 및 고장 예측 (failure prediction)을 수행하고, 안정적인 5G 가상네트워크 관리에 필수적인 Auto-scaling, Live Migration 등의 기능을 적용하여 과부하와 서비스 품질저하를 완화하거나 고장을 사전에 예방하는 NFV 관리 시스템을 제안한다.
Leader’s Role in Fostering Creativity: The Creativity Creation Model at KT AIT
문성욱,신재호,양홍석,JAMES WON-KI HONG 서울대학교 경영연구소 2016 Seoul Journal of Business Vol.22 No.1
To achieve innovation, constraints that block the effect of a company’s creative culture on innovation and creativity in the organization have to be removed. We propose the creativity creation model that takes account of these constraints and suggest that, to cultivate an innovative climate,leaders should receive feedback from innovation performance. To improve creativity and achieve successful innovation, leaders should be involvedin every procedure of the model. We explore the three main procedures of the model and present a practical application to the case of a Korean telecommunications company’s research and development institute, KT AIT.
Oversampling Techniques for Detecting Bitcoin Illegal Transactions
Jungsu Han,Jongsoo Woo,Jame Won-Ki Hong 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
Bitcoin users are guaranteed to be anonymous, increasing the number of cryptocurrency trading related to crimes and fraudulent activities. While most studies about detecting illegal transactions try to distinguish trading patterns and classify them from legitimate ones, classification performance is poor since the class distributions of transaction data are highly imbalanced. In general, the Synthetic Minority Over-sampling TEchnique (SMOTE) is used to deal with class-imbalanced data, but SMOTE has a problem that it does not fully represent the diversity of the data. In this paper, we introduce another oversampling technique using Generative Adversarial Networks (GAN) to generate artificial training data for classification model. In order to verify similarity between artificial data and the actual one, oversampled dataset is evaluated with a classification model using XGBoost algorithm. We show classification performance is improved on average with synthetic data generated by both SMOTE and well-designed GAN model.
Personalized Battery Lifetime Prediction for Mobile Devices based on Usage Patterns
Kang, Joon-Myung,Seo, Sin-Seok,Hong, James Won-Ki Korean Institute of Information Scientists and Eng 2011 Journal of Computing Science and Engineering Vol.5 No.4
Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.