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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Huang, Xinyu Liao, Xingxing Yang, Jie You, Wei Wu, Wenhao Min, Shiru Ji, Xinsheng Southeast University School of Cyber Science and Engineering Nanjing China Purple Mountain Laboratories Nanjing China National Digital Switching Engineering & Technological R&d Center Zhengzhou China Information Engineering University Zhengzhou China

As containerization technology progresses, the network functions within the 5G core are abstracted into Virtual Network Functions (VNFs), operating within a cloud-based ecosystem. To bolster network resilience, autoscaling strategies dynamically allocate resources in response to workloads, ensuring the Quality of Service (QoS) is maintained. However, the inherent openness of 5G networks exposes them to signaling storms and unexpected disturbances, leading to substantial workload fluctuations and bursts. Current reactive autoscaling strategies are slow to respond to these challenges, falling short of user expectations for low-latency, high-quality internet services. To address this challenge, we propose a novel auto-scaling framework specifically designed for the 5G cloud core network system, dubbed 5G-PPdE. Our framework harnesses the Crossformer model for accurate workload forecasting and employs a Multi-Layer Perceptron (MLP) classification model to offer four distinct scaling strategies. The scaling-down decision algorithm implements a Gradual decrease Strategy (GdS) for resource management, while the burst scaling strategy adapts to sudden workload changes by referencing the maximum service quantities observed during historical window periods. We have constructed a validation system leveraging the open-source Free5GC and Free5GMANO, using the wrk tool to simulate burst traffic scenarios. Prometheus is utilized for real-time data collection, thereby validating the efficacy of our proposed framework. The findings demonstrate that our method effectively mitigates the impact of burst traffic, outperforming current solutions. © 2024 IEEE.

关键词： Network function virtualization

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5G-PPdE: A Novel Adaptive Scaling Framework for Enhancing the resilience of the 5G Cloud Core Network

5G-PPDE: A Novel Adaptive Scaling Framework for Enhancing th...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xinyu Huang Xingxing Liao Jie Yang Wei You Wenhao Wu Shiru Min Xinsheng Ji School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China National Digital Switching Engineering & Technological R&D Center Zhengzhou China Information Engineering University Zhengzhou China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

关键词： Cloud computing Adaptation models Adaptive systems 5G mobile communication Quality of service Predictive models Security resource management Forecasting resilience

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domain-Oriented Software defined Computing Architecture

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China Communications 2019年第6期16卷 162-172页

作者： Ping Lv Qinrang Liu Hongchang Chen Ting Chen National Digital Switching System Engineering and Technical R&D Center

With the introduction of software defined hardware by dArPA Electronics resurgence Initiative,software definition will be the basic attribute of information *** from boundary certainty and algorithm aggregation of domain applications,domain-oriented computing architecture has become the technical direction that considers the high flexibility and efficiency of information *** at the characteristics of data-intensive computing in different scenarios such as Internet of Things(IoT),big data,artificial intelligence(AI),this paper presents a domain-oriented software defined computing architecture,discusses the hierarchical interconnection structure,hybrid granularity computing element and its computational kernel extraction method,finally proves the flexibility and high efficiency of this architecture by experimental comparison.

关键词： software defined hardware software defined computing architecture hierarchical interconnection mixed-granular computing element

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deepWeave: Accelerating job completion time with deep reinforcement learning-based coflow scheduling 29

DeepWeave: Accelerating job completion time with deep reinfo...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Sun, Penghao Guo, Zehua Wang, Junchao Li, Junfei Lan, Julong Hu, Yuxiang National Digital Switching System Engineering and Technological R&D Center Beijing Institute of Technology China

ISBN: (纸本)9780999241165

To improve the processing efficiency of jobs in distributed computing, the concept of coflow is proposed. A coflow is a collection of flows that are semantically correlated in a multi-stage computation task. A job consists of multiple coflows and can be usually formulated as a directed-Acyclic Graph (dAG). A proper scheduling of coflows can significantly reduce the completion time of jobs in distributed computing. However, this scheduling problem is proved to be NP-hard. different from existing schemes that use hand-crafted heuristic algorithms to solve this problem, in this paper, we propose a deep reinforcement Learning (drL) framework named deepWeave to generate coflow scheduling policies. To improve the inter-coflow scheduling ability in the job dAG, deepWeave employs a Graph Neural Network (GNN) to process the dAG information. deepWeave learns from the history workload trace to train the neural networks of the drL agent and encodes the scheduling policy in the neural networks, which make coflow scheduling decisions without expert knowledge or a pre-assumed model. The proposed scheme is evaluated with a simulator using real-life traces. Simulation results show that deepWeave completes jobs at least 1.7× faster than the state-of-the-art solutions. © 2020 Inst. Sci. inf., Univ. defence in Belgrade. All rights reserved.

关键词： reinforcement learning

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Lightweight and fast coordinated update algorithm for hybrid SdN networks 4th

Lightweight and fast coordinated update algorithm for hybrid...

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4th International Conference on Intelligent Computing, Communication and devices, ICCd 2018

作者： Yu, Changhe Lan, Julong Hu, Yuxiang National Digital Switching System Engineering & Technological R&D Center Zheng Zhou450002 China

ISBN: (纸本)9789811394058

Hybrid software-defined networking (SdN) networks are intermediates between traditional networks and SdN networks. However, since the hybrid control planes are structurally different from the single control plane, updating the hybrid SdN networks lacks effective techniques. In this paper, by utilizing the segment routing (Sr) mechanism to update hybrid SdN networks and formalize the application of Sr during updates, we propose a cooperative consistency update algorithm that can coordinate Sr, two-phase commit techniques. The algorithm is capable of preserving consistency with low overhead and excellent applicability. When a network requires updating, the algorithm leverages the Sr mechanism to stitch a new path using the available path segments;thus, flows can use the new path immediately. If the update cannot be completed with the Sr mechanism alone, the algorithm can utilize the two-phase commit technique to update the nodes through duplication. Notably, our experiments based on real topologies indicate that, compared to prior techniques, the proposed algorithm can greatly reduce the TCAM overhead. © Springer Nature Singapore Pte Ltd 2020.

关键词： Consistency Hybrid SdN Network updates

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Preface: Security and safety in physical layer systems

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Security and Safety 2024年第1期3卷 4-6页

作者： Aiqun Hu Liang Jin Xiangyun Zhou Feng Shu Xiangwei Zhou State Key Laboratory of Mobile Communications Southeast UniversityNanjing 210096China National Digital Switching System Engineering&Technological R&D Center Zhengzhou 450002China School of Engineering Australian National UniversityCanberra 2601Australia School of Information and Communication Engineering Hainan UniversityHaikou Hainan 570100China Division of Electrical and Computer Engineering Louisiana State UniversityBaton Rouge 70803USA

This special topic mainly focuses on the progress of physical-layer security(PLS)technologies and their potential applications for the future beyond fifth-generation(B5G)and sixth-generation(6G)*** tremendous growth in connectivity and the ubiquity of wireless communications have resulted in an unprecedented awareness of the importance of security and *** secure and trusted communications is vital for future intelligent connected applications,especially life-critical vehicle-toeverything(V2X)***,the heterogeneous,dynamic and decentralized architecture of these networks leads to difficulties for cryptographic key management,and *** exploiting the physical characteristics of devices,wireless channels and noise,PLS offers reliable solutions against eavesdropper attacks as complementary approaches to cryptographic techniques.

关键词： layer complementary networks

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Unified Accelerator for Attention and Convolution in Inference Based on FPGA

Unified Accelerator for Attention and Convolution in Inferen...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Tianyang Li Fan Zhang Xitian Fan Jianliang Shen Wei Guo Wei Cao Information Engineering University Zhengzhou China National Digital Switching System Engineering and Technological R&D Center Zhengzhou China Shanghai HONGZHEN Information Science & Technology Corporation Shanghai China Institute for Big Data Fudan University Shanghai China

Many models combining Transformers with convolutional neural networks (CNNs) for computer vision tasks have achieved state-of-the-art results. However, due to the different computation patterns between attention and convolution, using a dedicated Transformer or CNN accelerator will inevitably reduce the computing efficiency of the other. To overcome this problem, we propose a unified architecture for attention and convolution on FPGA. We reduce runtime overhead by offloading part of self-attention computations offline before inference. Furthermore, we present a unified mapping method according to the computing characteristics of attention-based and convolution-based models. This accelerator implements multi-head attention in Transformer, independent resNet-50 and hybrid blocks of attention and con-volution in BoTNet-50 at 200MHz on Xilinx Virtex Ultrascale+ XCVU37P. Experimental results show that the solution is nearly 3.62 times more energy-efficient than the NVIdIA V100 GPU, and the computational efficiency is 11.86% and 28.29% higher than the state-of-the-art Transformer and resNet-50 accelerators, respectively.

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CLMSI: A Novel Image-Text Named Entity recognition Method Based on Contrast Learning and Multimodal Semantic Interaction

CLMSI: A Novel Image-Text Named Entity Recognition Method Ba...

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International Conference on Behavior, Economic and Social Computing (BESC)

作者： Jiangtao Ma Wenduo Jin Yonggang Chen Fan Zhang Shenyi Qian Yaqiong Qiao College of Computer and Information Engineering Tianjin Normal University Tianjin China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China The State Information Center China National Digital Switching System Engineering and Technology R&D Center Zhengzhou China College of Cyber Science Nankai University Tianjin China

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

Most existing multimodal named entity recognition (MNEr) methods cannot align image and text well, and fail to effectively fuse image-text semantic information, leading to suboptimal MNEr accuracy. To this end, this paper proposes a novel named entity recognition model based on Contrastive Learning and Multimodal Semantic Interaction (named CLMSI). Specifically, to narrow the semantic gap between image-text modalities, making semantically similar image-text representations closer and pushing dissimilar ones further apart, we propose a contrastive learning-based cross-modal alignment method. Moreover, to integrate multimodal information effectively and filter out the noise introduced by image features as thoroughly as possible, we propose to incorporate image descriptions as additional features to enrich the multimodal representation and to reduce the semantic bias of the image information. Extensive experiments conducted on two MNEr benchmark datasets, Twitter-2015 and Twitter-2017, demonstrate the superiority of the proposed method. Further ablation studies and case analyses also affirm the efficacy of CLMSI.

关键词： Visualization Social computing Accuracy Social networking (online) Computational modeling Semantics Noise Named entity recognition Feature extraction data mining

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An Incremental dynamic Community detection Algorithm Based on Node Participation

An Incremental Dynamic Community Detection Algorithm Based o...

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IEEE International Conference on Cloud Computing and Big data Analysis (ICCCBdA)

作者： Hui Li Fucai Chen Jianpeng Zhang National Digital Switching System Engineering & Technological R & D Center Information Engineering University Zhengzhou China

Many networks in the real world are dynamically changing over time, and the community structure also changes accordingly. The community detection in the dynamic and constantly evolving network has received great attention from researchers. In order to uncover the community structure in the dynamic network, this paper proposes an incremental dynamic community detection algorithm based on node participation (NPICd). It defines the node participation to the community and the community fitness. In response to the changes of nodes and edges in the network, different update strategies are proposed to discover the community at different moments in an incremental manner. The experimental results on the real network show that compared with several widely used methods, our algorithm can quickly and accurately mine the community structure in the dynamic network.

关键词： Cloud computing Atmospheric measurements Heuristic algorithms Image edge detection Conferences Clustering algorithms Big data

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defending Against Link Failure in Virtual Network Embedding Using a Hybrid Scheme

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China Communications 2019年第1期16卷 129-138页

作者： Jianjian Ai Hongchang Chen Zehua Guo Guozhen Cheng National Digital Switching System Engineering & Technological R&D Center University of Minnesota Twin Cities

Nowadays network virtualization is utterly *** a result,how to protect the virtual networks from attacking on the link is increasingly *** schemes are mainly backup-based,which suffer from data loss and are helpless to such attacks like data *** offer high security level,in this paper,we first propose a multipath and decision-making(Md) scheme which applies multipath simultaneously delivery and decision-making for protecting the virtual *** different security requirement for virtual link,we devise a hybrid scheme to protect the virtual *** the critical links,Md scheme is *** the other links,we adopt the Shared Backup *** simulation results indicate the proposed scheme can significantly increase the security level of the critical link high in the loss of less acceptance ratio.

关键词： network virtualization virtual link security multipath and decision-making hybrid scheme

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