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research on Cloud-Native Monitoring System Based on Prometheus 4

Research on Cloud-Native Monitoring System Based on Promethe...

引用

4th International Conference on Sensors and Information Technology, ICSI 2024

作者： Liu, Yajun Yu, Zhitao Wang, Qian Mei, Hong Song, Guolin Li, Haiou Network Planning and Design Optimization Research and Development Center China Mobile Group Design Institute Company Limited Beijing China Wuhan Branch Network Planning and Design Optimization Research and Development Center China Mobile Group Design Institute Company Limited Wuhan China Hubei Branch China Mobile Group Design Institute Company Limited Wuhan China

ISBN: (数字)9781510678699

ISBN: (纸本)9781510678682

With the continuous evolution of enterprise cloudification and digitalization, cloud-native technologies such as microservices and containerization have gradually become industry standards. However, the rapid iteration of microservices applications presents new challenges to system stability, putting pressure on maintenance personnel. This paper focuses on the open-source Prometheus monitoring ecosystem and designs a monitoring and alerting system that adapts to distributed large-scale cluster monitoring and multi-tenant management, providing an integrated monitoring solution for IT infrastructure, microservices, and containers. The system has been successfully deployed in real production environments by China Mobile Group. Through this system, maintenance personnel can quickly identify issues in business systems and provide high availability services. © 2024 SPIE.

关键词： System stability

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Application of Federated Learning in mobile communication Anti-telecom fraud

Application of Federated Learning in mobile communication An...

引用

2023 International Conference on Communication network and Machine Learning, CNML 2023

作者： Li, Haiou Kang, Zhiwen Song, Guolin Liu, Yajun Mei, Hong Chen, Xin Gu, Ming Wang, Jieli Chen, Geng Network Planning and Design Optimization Research and Development Center China Mobile Group Design Institute Company Limited Beijing China China Mobile Group Design Institute Co. Ltd. Shandong Branch Jinan China Wuhan Branch Network Planning and Design Optimization Research and Development Center China Mobile Group Design Institute Company Limited Wuhan China

ISBN: (纸本)9798400716683

In order to solve the problem that telecom fraud data cannot be centralized for training due to privacy security and data island in the training of telecom fraud model in mobile communication, federated learning can be integrated into the network information interaction to construct an efficient and high-security data model training architecture. The distributed deep learning model training architecture based on federated learning is introduced, and the research status and standard application methods of federated learning are summarized. Based on the model training architecture, an application case test is carried out, and the traditional model training methods are compared. Finally, it is proved that this method is superior to the existing centralized model training method in terms of security and training efficiency under the condition of ensuring network patency. © 2023 ACM.

关键词： Deep learning

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research and Application of Automated Intelligent planning Technology for Engineering Parameters of Communication Base Stations 7

Research and Application of Automated Intelligent Planning T...

引用

7th International Conference on Communication and Information Systems, ICCIS 2023

作者： Xiang, Zhongqiu Li, Binjie Huang, Xuemin Wang, Wei Chen, Fan Liu, Xuecheng Wang, Yunjing Hou, Shengli China Mobile Group Design Institute Co. Ltd. Network Optimization Products Department Beijing China China Mobile Communications Group Co. Ltd. Network Business Department Beijing China Wireless Network Optimization Center China Mobile Group Guangdong Co. Ltd. Guangzhou China Wuhan Branch China Mobile Group Design Institute Co. Ltd. Business and It Development Department Wuhan China China Mobile Group Design Institute Co. Ltd. Business and It Development Department Beijing China Digital Intelligence Planning and Design Center China Mobile Group Design Institute Co. Ltd. Beijing China

ISBN: (纸本)9798350330540

The engineering parameters of communication base stations are the core assets of telecommunication operators. It directly determines the quality of the network and the perception of users. The current communication network has accumulated a large amount of wireless network big data, and adjusting base station engineering parameters based on user business data is an important scientific issue. This article proposes a technical solution for engineering parameter planning based on simulation iteration and artificial intelligence algorithms. This scheme is based on high-precision simulation, so it can predict the optimization effect in advance. This scheme adopts particle swarm optimization algorithm for intelligent optimization, achieving the goal of effectively improving the optimization effect. At the same time, we combine existing wireless big data to achieve precise engineering parameter planning based on the distribution of user business volume. Therefore, it can achieve the effect of stimulating traffic and maximizing investment return ratio. Furthermore, it is possible to achieve self intelligent network operation and maintenance. Application practice has shown that adopting this technical solution can reduce base station resource investment by 10% and improve network quality by 19.9%, which is of great significance for actual network operation and maintenance. © 2023 IEEE.

关键词： Automated Intelligent network Engineering parameter planning High precision simulation Particle Swarm optimization Stimulate traffic

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Spatial-Temporal Attention Graph WaveNet for Traffic Forecasting 5

Spatial-Temporal Attention Graph WaveNet for Traffic Forecas...

引用

5th International Conference on Data-Driven optimization of Complex Systems, DOCS 2023

作者： Liu, Shixuan Zhu, Jijun Lei, Wei Zhang, Ping School of Artificial Intelligence Hebei University of Technology Tianjin China Hebei Provincial Communications Planning Design and Research Institute Co. Ltd. Research and Development Center of Transport Industry of Self-driving Technology Chelu Cloud Network Hebei Provincial Engineering Research Center Hebei Shijiazhuang China

ISBN: (纸本)9798350393521

Accurately predicting road traffic conditions is critical for travel planning and efficient allocation of resources, making it a major research focus within smart cities. However, traffic forecasting poses significant challenges due to the complex spatial correlations and nonlinear temporal dependencies inherent to real-time traffic networks. Existing approaches for modeling spatial dependencies have primarily relied on a fixed graph structure, assuming that the relationships between roads are predetermined and static. In contrast, real-world transportation networks exhibit more dynamic interactions that evolve over time. To address this issue, we propose a Spatial-Temporal Attention Graph WaveNet (STAGWN) model for complex traffic network modeling. STAGWN utilizes self-attention network to capture dynamic spatial dependencies. Additionally, long time series are processed via temporal convolution module consisting of stacked dilated casual convolutions in STAGWN. Furthermore, STAGWN incorporates spatial and temporal embeddings to capture spatial-temporal heterogeneity. The proposed model is evaluated using two real-world datasets and compared with 10 baseline methods. Results indicate our approach attains state-of-the-art for 30 and 60 minutes ahead forecasts, demonstrating efficacy on longer prediction horizons. © 2023 IEEE.

关键词： Convolution

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Long Short Dynamic Graph Neural network for Traffic Flow Forecasting 5

Long Short Dynamic Graph Neural Network for Traffic Flow For...

引用

5th International Conference on Data-Driven optimization of Complex Systems, DOCS 2023

作者： Guo, Liuyang Wu, Huicai Jiao, Yanli Zhang, Ping School of Artificial Intelligence Hebei University of Technology Tianjin China Hebei Provincial Communications Planning Design and Research Institute Co. Ltd. Research and Development Center of Transport Industry of Self-driving Technology Chelu Cloud Network Hebei Provincial Engineering Research Center Hebei Shijiazhuang China

ISBN: (纸本)9798350393521

Accurate traffic flow forecasting plays a crucial role as the foundation for providing high-quality services in intelligent transportation systems. Due to the complex and long-range spatial temporal dependencies in traffic network and the dynamic variations in traffic flow between roads, traffic flow forecasting is a challenging task. Existing research has made significant progress in modeling complex spatial temporal correlations in traffic data using spatial temporal graph neural networks. However, they often only utilize a single module for modeling temporal dependencies, which ignores the distinction of the temporal dependencies. In fact, traffic flow forecasting involves short-term and long-term forecasting. Therefore, distinguishing temporal dependencies between different time periods can capture temporal information more comprehensively. Furthermore, effectively modeling the dynamics and spatial-temporal heterogeneity of traffic data remains a challenge. To address these issues, we propose a novel Long Short Dynamic Graph Neural network (LSDGNN) for traffic flow forecasting. We introduce a Long Short Temporal Convolution module (LSTConv) that separately models short-term and long-term dependencies in traffic data and then fuses them, allowing effective learning of both short-term and long-term temporal dependencies. Additionally, to model the dynamics of traffic data, we capture spatial dependencies at different times, nodes, and traffic scenarios, enabling the modeling of spatial dynamics. We evaluate our proposed method on several popular datasets, and the results demonstrate that the LSDGNN framework significantly outperforms state-of-the-art methods. © 2023 IEEE.

关键词： Convolution

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research and Application of Automated Intelligent planning Technology for Engineering Parameters of Communication Base Stations

Research and Application of Automated Intelligent Planning T...

引用

IEEE International Conference on Communication and Information Systems (ICCIS)

作者： Zhongqiu Xiang Binjie Li Xuemin Huang Wei Wang Fan Chen Xuecheng Liu Yunjing Wang Shengli Hou Network Optimization Products Department China Mobile Group Design Institute Co. Ltd. Beijing China Network Business Department China Mobile Communications Group Co. Ltd. Beijing China Wireless Network Optimization Center China Mobile Group Guangdong Co. Ltd. Guangzhou China Business and IT Development Department Wuhan Branch China Mobile Group Design Institute Co. Ltd. Wuhan China Business and IT Development Department China Mobile Group Design Institute Co. Ltd. Beijing China Digital Intelligence Planning and Design Center China Mobile Group Design Institute Co. Ltd. Beijing China

The engineering parameters of communication base stations are the core assets of telecommunication operators. It directly determines the quality of the network and the perception of users. The current communication network has accumulated a large amount of wireless network big data, and adjusting base station engineering parameters based on user business data is an important scientific issue. This article proposes a technical solution for engineering parameter planning based on simulation iteration and artificial intelligence algorithms. This scheme is based on high-precision simulation, so it can predict the optimization effect in advance. This scheme adopts particle swarm optimization algorithm for intelligent optimization, achieving the goal of effectively improving the optimization effect. At the same time, we combine existing wireless big data to achieve precise engineering parameter planning based on the distribution of user business volume. Therefore, it can achieve the effect of stimulating traffic and maximizing investment return ratio. Furthermore, it is possible to achieve self intelligent network operation and maintenance. Application practice has shown that adopting this technical solution can reduce base station resource investment by 10 % and improve network quality by 19.9%, which is of great significance for actual network operation and maintenance.

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Long Short Dynamic Graph Neural network for Traffic Flow Forecasting

Long Short Dynamic Graph Neural Network for Traffic Flow For...

引用

Data-driven optimization of Complex Systems (DOCS), International Conference on

作者： Liuyang Guo Huicai Wu Yanli Jiao Ping Zhang School of Artificial Intelligence Hebei University of Technology Tianjin China Research and Development Center of Transport Industry of Self-driving Technology Chelu Cloud Network Hebei Provincial Engineering Research Center Hebei Provincial Communications Planning Design and Research Institute Co. Ltd. Shijiazhuang Hebei China

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Spatial-Temporal Attention Graph WaveNet for Traffic Forecasting

Spatial-Temporal Attention Graph WaveNet for Traffic Forecas...

引用

Data-driven optimization of Complex Systems (DOCS), International Conference on

作者： Shixuan Liu Jijun Zhu Wei Lei Ping Zhang School of Artificial Intelligence Hebei University of Technology Tianjin China Research and Development Center of Transport Industry of Self-driving Technology Chelu Cloud Network Hebei Provincial Engineering Research Center Hebei Provincial Communications Planning Design and Research Institute Co. Ltd. Shijiazhuang Hebei China

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Semi-supervised Subspace Metric Learning 3

Semi-supervised Subspace Metric Learning

引用

3rd IEEE International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021

作者： Chen, Yanlei Zhao, Fangrong Zhang, Nijing Zhou, Chaozheng RD Center for Network Planning and Design Optimization China Mobile Group Design Institute Co. Ltd China Shanghai University School of Science Department of Mathematics Shanghai China China Mobile Shanghai ICT Co. Ltd. Shanghai China Shanghai Electric Central Research Institute Shanghai China

ISBN: (纸本)9781665406055

Metric learning is a popular approach for measuring the similarity between samples and is essential for many machine learning tasks. However, its performance may be degraded when the data is corrupted by noise, the feature is redundant, or the data is heterogeneously distributed. To circumvent these limitations, we propose a semi-supervised subspace local metric learning algorithm. Specifically, we find a low-dimensional subspace by regarding the subspace learning as an inverse problem on Grassmannian manifold. Then we learn some local positive definite metrics on the subspace defined on different regions and optimize them using intrinsic steep decent method. We also introduce a manifold regularizer to preserve the data geometric structure. At last, our experiments show that, even on noisy datasets, we still improve the accuracy and robustness compared with the state-of-the-art metric learning algorithms. © 2021 IEEE.

关键词： Learning algorithms

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Estimation method of vehicle lane by millimeter wave radar 3

Estimation method of vehicle lane by millimeter wave radar

引用

3rd International Conference on Computer Science and Communication Technology, ICCSCT 2022

作者： Li, Chunjie Lei, Wei Jin, Ye Zhang, Long Zhao, Kai Hebei Provincial Communications Planning Design and Research Institute Co. Ltd. Hebei China Research and Development Center of Transport Industry of Self-driving Technology Hebei China Southeast University Jiangsu China Industrial Research Institute of Vehicle-Road-Cloud-Network Integration Hebei China Chongqing Innovation Center of Beijing Institute of Technology Chongqing China Beijing Institute of Technology Ruixing Electronic Technology Co. Ltd. Beijing China

ISBN: (纸本)9781510661240

For the current smart high-speed scene, the millimeter-wave radar needs to give the lane number of the vehicle while outputting the lane-level target track information. Since the millimeter-wave radar cannot identify the lane line information, it is difficult to achieve the lane estimation without the help of other sensors. This paper proposes a method to quickly estimate the lane numbers of vehicles in different high-speed scenarios with the help of high-precision maps. First, it unifies the map and vehicle track coordinate system, converts the point set of lane line in WGS-84 format to the Cartesian coordinate system with the radar as the origin after coordinate transformation. Then, through Lagrange interpolation, the X value corresponding to the Y value of the vehicle on the left and right side of the lane line is obtained. Finally, the lane to which the vehicle belongs is estimated by the ratio method. The actual measurement results show that the method can quickly and accurately estimate the lane number of the vehicle in various high-speed scenarios without the help of other sensors. © 2022 SPIE.

关键词： Vehicles

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