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HybridDep: An elastic hybrid resources allocation strategy for I/O-intensive applications

IET COMMUNICATIONS 2025年第1期19卷

作者： Li, Pengmiao Zhang, Yuchao Yun, Shaoxuan Yu, Fucai Wu, Aizhi Beijing Acad Safety Sci & Technol Urban Operat Safety Res Ctr Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

Along with the rapid development of B5G/6G, the number of applications grows rapidly and the data amount explodes exponentially, putting a massive burden on the resource-limited edge servers. To fully utilize the limited resources, virtualization technology is introduced to provide elastic deployment for applications in edge servers. But for I/O-intensive applications, allocating elastic resources is not as easy as for compute-intensive ones, because the amount of required I/O resources is unknown due to the request uncertainty. Many existing researches try to solve this multi-application deployment problem by peaks clipping and valleys filling, to resource utilization. However, in fact, the times of peaks and valleys of most hybrid deployed applications are similar to each other, which invalidates those traditional solutions. To address this challenge, the actual data is analysed and complementary peak and valley periods in time and space dimensions are found. Based on this finding, an elastic hybrid deployment strategy HybridDep is proposed, for multiple I/O-intensive applications. Validated by simulation experiments using real datasets and traces, this algorithm can reduce about 3.2% deployment cost than the compared algorithm.

关键词： bandwidth allocation resource allocation

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Joint Optimization of Convergence and Latency for Hierarchical Federated Learning Over Wireless Networks

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第3期13卷 691-695页

作者： Sun, Haofeng Tian, Hui Zheng, Jingheng Ni, Wanli Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

This letter investigates an hierarchical federated learning (HFL) framework with partial aggregation in a resource-constrained multi-tier wireless network. Due to device heterogeneity and resource scarcity, device selection and resource allocation are jointly considered in the presence of inter-cell interference (ICI). To capture the impacts of device selection and resource allocation on the convergence behavior of HFL, an upper bound of the expected optimality gap is derived. Then, a mixed-integer non-linear programming (MINLP) problem is formulated to minimize the weighted sum of the optimality gap and overall latency. To solve the MINLP problem efficiently, a two-layer solution based on genetic algorithm and alternating optimization is designed to jointly optimize the resource block association, uplink transmission power, and central processing unit frequency allocation. Simulation results on two real-world datasets demonstrate that the proposed algorithm achieves better learning performance and ICI management than the federated learning with client selection (FedCS) and random resource block association schemes.

关键词： Ink Resource management Wireless networks Convergence Training Performance evaluation Energy consumption Hierarchical federated learning wireless networks convergence analysis resource allocation

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Scheduling of Digital Twin Synchronization in Industrial Internet of Things: A Hybrid Inverse Reinforcement Learning Approach

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 5137-5147页

作者： Zhang, Qiuyang Wang, Ying Li, Zhendong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China

The digital transformation of industrial systems has been significantly influenced by the emergence of the Industrial Internet of Things. Digital twin (DT) technology plays a pivotal role in the transformation, serving as a bridge between the physical and digital realms. To support the efficient application of DT technology, the synchronization between physical entities (PEs) and DT models (DTMs) can not be ignored. Given the open nature of wireless channels, the diversity in synchronization mechanisms arising from the functional variations among PEs inevitably deteriorates the design of synchronization strategies. Moreover, a metric is required to gauge the synchronization between PEs and DTMs. In this article, a reinforcement learning (RL)-based online scheduling scheme is proposed to achieve efficient synchronization between DTMs and PEs with different mechanisms. Specifically, the Age of Information (AoI) is introduced as a metric to evaluate synchronization strategies. In addition, PEs are classified and share spectrum resources according to synchronous mechanisms, improving resource utilization efficiency. To ensure real-time scheduling, we propose a hybrid inverse RL-based scheme to support distributed time slot-level synchronization, reducing the need for manual intervention. Simulation results show that compared with other baseline RL schemes, the proposed scheme can reduce the AoI value more than 20% between the PE and the DTM.

关键词： Synchronization Digital twins Servers Production Wireless communication Internet of Things Optimization Job shop scheduling Industrial Internet of Things Cloud computing Age of Information (AoI) digital twin (DT) inverse reinforcement learning (RL) spectrum sharing

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LogEDL: Log Anomaly Detection via Evidential Deep Learning

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APPLIED SCIENCES-BASEL 2024年第16期14卷

作者： Duan, Yunfeng Xue, Kaiwen Sun, Hao Bao, Haotong Wei, Yadong You, Zhangzheng Zhang, Yuantian Jiang, Xiwei Yang, Sangning Chen, Jiaxing Duan, Boya Ou, Zhonghong China Mobile Commun Grp Co Ltd Beijing 102206 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

With advancements in digital technologies such as 5G communications, big data, and cloud computing, the components of network operation systems have become increasingly complex, significantly complicating system monitoring and maintenance. Correspondingly, automated log anomaly detection has become a crucial means to ensure stable network operation and protect networks from malicious attacks or failures. Conventional machine learning and deep learning methods assume consistent distributions between the training and testing data, adhering to a closed-set recognition paradigm. Nevertheless, in realistic scenarios, systems may encounter new anomalies that were not present in the training data, especially in log anomaly detection. Inspired by evidential learning, we propose a novel anomaly detector called LogEDL, which supervises the training of the model through an evidential loss function. Unlike traditional loss functions, the evidential loss function not only focuses on correct classification but also quantifies the uncertainty of predictions. This enhances the robustness and accuracy of the model in handling anomaly detection tasks while achieving functionality similar to open-set recognition. To evaluate the proposed LogEDL method, we conduct extensive experiments on three datasets, i.e., HDFS, BGL, and Thunderbird, to detect anomalous log sequences. The experimental results demonstrate that our proposed LogEDL achieves state-of-the-art performance in anomaly detection.

关键词： log anomaly detection evidential deep learning uncertainty

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Semantic Clone Detection Based on Code Feature Fusion Learning

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2023年第7期33卷 1039-1062页

作者： Zhang, Qianjin Jin, Dahai Wang, Yawen Gong, Yunzhan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Guangxi Key Lab Cryptog & Informat Secur Guilin 541004 Guangxi Peoples R China

Code clones are duplicated code snippets that significantly threaten software maintenance and the public corpora of code representation learning. Traditionally, code context and its structure information abstract syntax tree (AST), control flow graph (CFG) are typical representations of source code, and context-based models and structure-based models contributed significantly to the development of code clone detection. In this paper, we present a hybrid embedding model for code clone detection (HEM-CCD), a fusion method of token sequential information and graph-based structure information. We insert tokens' global context information encoded by a bi-directional recurrent neural network into the AST-based graph for comprehensive code semantic representation. Then, feeding the graph into a gated graph neural network we generate code semantic vectors for similarity evaluation. We have implemented our model on two public clone datasets (BigCloneBench and GoogleCodeJam), and the results indicate that HEM-CCD outperforms several state-of-the-art approaches.

关键词： Code clone detection code representation learning code semantic understanding graph neural network

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Forward privacy multikeyword ranked search over encrypted database

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第10期37卷 7356-7378页

作者： Zhao, Shaohua Zhang, Hua Zhang, Xin Li, Wenmin Gao, Fei Wen, Qiaoyan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Dynamic searchable encryption (SE) aims at achieving varied search function over encrypted database in dynamic setting, which is a trade-off in efficiency, security, and functionality. Recent work proposes a file-injection attack which can successfully attack by utilizing some information leaked in the update process. To mitigate this attack, some SE schemes with forward privacy are proposed. However, these schemes are designed to achieve single keyword or conjunctive keyword search, which cannot support multikeyword search. Moreover, these schemes do not consider the function of results ranking. In this paper, we propose a forward privacy multikeyword ranked search scheme over encrypted database. We design a forward privacy multikeyword search scheme based on the classic MRSE scheme. Our scheme makes the cloud cannot obtain the actual match results of the past query with the newly updated files by adding the well-chosen dummy elements to the original index and query vectors. We rank the search results based on the matched keyword number and the T F x I D F $TF\times IDF$ rule in the dynamic setting. Our scheme uses only the symmetric encryption primitive. We implement our scheme for COVID-19 data set and the experimental evaluation results show that the proposed scheme is secure and efficient.

关键词： data security forward privacy multikeyword search result ranking searchable encryption

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Two grids are better than one: Hybrid indoor scene reconstruction framework with adaptive priors

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NEUROCOMPUTING 2025年 618卷

作者： Bai, Boyuan Qiao, Xiuquan Lu, Ping Zhao, Hongru Shi, Wenzhe Zhang, Hengjia Huang, Yakun Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China State Key Lab Mobile Network & Mobile Multimedia T Shenzhen 518055 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China

Indoor scene reconstruction from multi-view images is a pivotal technology within the field of robotics and augmented reality. Previous researches have predominantly focused on neural radiance fields aided by geometric monocular priors. However, due to the inductive smoothness bias introduced by deep Multi- Layer Perceptron (MLP) networks, these methods struggle to recover the scene surface with complex and fine geometry details. Additionally, when used as additional supervision signals during optimization, priors indifferent regions make different contributions. Simply incorporating them in all regions may lead to a decrease in the accuracy. To tackle these issues, we present a generic end-to-end framework named AdaptSurf, which combines Signed Distance Field (SDF) voxel grids and feature voxel grids to enhance the capability of reconstructing accurate geometry details, respectively. Furthermore, we design a policy network to adaptively enable the estimated depth or normal priors to supervise the learning process, which improves the reconstruction accuracy and accelerates neural surface reconstruction. Qualitative and quantitative experiments show that AdaptSurf yields high-quality surfaces, especially for fine-grained details and smooth regions. Furthermore, the policy network exhibits an interpretable behavior that depends on the voxel features, which helps to improve the quality of surface reconstruction.

关键词： 3D reconstruction NeRF Geometric priors Volume rendering

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Two-Layered Blockchain Architecture for Federated Learning over Mobile Edge Network

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IEEE NETWORK 2022年第1期36卷 45-51页

作者： Feng, Lei Yang, Zhixiang Guo, Shaoyong Qiu, Xuesong Li, Wenjing Yu, Peng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Federated learning (FL) is seen as a road towards privacy-preserving distributed artificial intelligence (AI) while keeping the raw training data on the local device. By leveraging blockchain, this paper puts forward a blockchain and FL fusioned framework to manage the security and trust issues when applying FL over mobile edge networks. First, a two-layered architecture is proposed that consists of two types of blockchains: local model update chain (LMUC) assisted by device-to-device (D2D) communication and global model update chain (GMUC) supporting task sharding. D2D-assisted LMUC is designed to chronologically and efficiently record all of the local model training results, which can help to form a long-term reputation of local devices. The GMUC is proposed to provide both security and efficiency by preventing mobile edge computing (MEC) nodes from malfunctioning and dividing it into logically-isolated FL task-specific chains. Then, a reputation-learning based incentive mechanism is introduced to make the participant local devices more trustful with the reward implemented by a smart contract. Finally, a case study is given to show that the proposed framework performs well in terms of FL learning accuracy and blockchain time delay.

关键词： blockchain federated learning security trustful mobile edge network

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Virtual Power Plant Energy Storage Device state Assessment System Based on 5G Base Stations 4

Virtual Power Plant Energy Storage Device State Assessment S...

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4th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Peng, Shan Xu, Peng Hu, Jiye Beijing University of Post and Telecommunications State Key Lab of Networking and Switching Technology Beijing China Beijing Chuangzhi Xinke Technology Co. Ltd Beijing China

ISBN: (纸本)9798350359589

In recent years, the integration of new energy devices into the power system to replace traditional sources has become a trend. Virtual power plants can effectively combine various distributed resources on the demand side. The energy storage devices in 5G base stations, with their widespread coverage and abundant dormant resources, can contribute to regulating power supply and demand within virtual power plants while ensuring the energy efficiency of the base stations. However, the complex geographical distribution and diverse performance of these energy storage devices require a more accurate assessment and secure management. This paper designs and implements a virtual power plant energy storage device state assessment system based on 5G base stations. The system is designed for the interaction of multi-source data from distributed energy storage devices, employing containerized deployment of multidimensional data analysis tasks. It effectively handles a vast amount of information from energy storage devices with varying performance, accomplishing functions such as capacity assessment and safety assessment. Additionally, the system provides a visual interface and safety prompts for power supply-demand analysis and regulation, validating the feasibility of a virtual power plant based on 5G base stations. Finally, comprehensive testing using simulated distributed real-time data collection ensures the effectiveness and timeliness of data acquisition, analysis, application, and energy storage device control. © 2024 IEEE.

关键词： Distributed Energy Storage Scalable Data Analysis Capabilities Virtual Power Plant

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In-Network DDoS Mitigation Mechanism for Vehicle Road Cooperation Network With Victim-Centric Approach

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IEEE INTERNET OF THINGS JOURNAL 2025年第11期12卷 15514-15532页

作者： Liu, Yan Shao, Sujie Guo, Shaoyong Zang, Zhibin Chen, Xingyu Qi, Feng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China State Grid Informat & Commun Ind Grp Co Ltd Beijing 102211 Peoples R China

Vehicle road cooperation (VRC) services closely related to personal safety impose stringent requirements on reliability and real-time performance. However, the growing trend of vehicle to network (V2N) connections has significantly intensified the threat of distributed Denial of Service (DDoS) to the end-cloud communication links. To address this challenge, this article introduces a comprehensive DDoS defense architecture for VRC, named in-network DDoS-oriented Balancer, Inspector, and Filter (IDBIF). Leveraging programmable switches, IDBIF adopts an in-network mode to dynamically mitigate volumetric DDoS traffic in real-time, which coordinates three data plane functions for traffic processing: 1) load balancing;2) packet inspection;and 3) traffic filtering. Our work faces the starved data plane resources and establishes the problem model for high reliability requirements of VRC services when subjected to DDoS attacks. Furthermore, we developed a multiagent hierarchical structure policy gradient (MAHSPG) algorithm that combines the inherent characteristics of DDoS attacks to tackle the runtime deployment of high-dimensional and heterogeneous defense function chains. Simulation experiment results show that the proposed approach alleviates the data plane resource bottleneck and improves the DDoS defense effect by 24.80%, highlighting its advantages in ensuring reliable VRC services.

关键词： Computer crime Denial-of-service attack Prevention and mitigation Collaboration Pipelines Real-time systems Filtering Roads Reliability Inspection Cloud service security distributed Denial of Service (DDoS) programmable switch vehicle road cooperation (VRC)

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