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VmPTP: Precise Time Protocol for Inter-VM Communication in embedded Virtualized Systems

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 897-910页

作者： Zhao, Sirong Xie, Guoqi Ma, Wenhong Ren, Wei Li, Kenli Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan Changsha410082 China Huawei Technology Co. Ltd. Central Software Institute Guangdong Shenzhen518129 China

System virtualization techniques are gradually applied to embedded systems with the performance improvement of embedded devices;however, there is a large time offset (i.e., deviation) of hundreds of ms between virtual machines (VMs) due to the drift of the virtualization abstraction layer and the jitter phenomenon. It is urgent to significantly reduce this offset by time synchronization techniques. Unfortunately, the current mainstream time synchronization software (e.g., Chrony and LinuxPTP) cannot be used in the embedded virtualized system connected by the fieldbus, such as controller area network (CAN);meanwhile, drift correction-based time synchronization techniques dedicated to CAN bus cannot be utilized for virtualized systems. In this study, we design a general vmPTP, which is a precise time protocol (PTP) for inter-VM communication in embedded virtualized systems based on Linux kernel-based VM (KVM). This is the first time synchronization work focused on embedded virtualized systems. We implement two vmPTP versions based on VirtIO and inter-VM shared memory (IVSHMEM) according to the four message (i.e., packet) exchanges of PTP. We propose an asymmetry compensation strategy to solve the problem of asymmetric rate of I/O operations in the message exchange process, which optimizes the offset computation. We further produce a convergence stopping approach which can continuously converge the offset until the offset is within the given bound and stop. We conduct experimental evaluations on both X86-64 and ARM64 architectures. For two versions of vmPTP, the experimental results show that: 1) the offsets are always within 1μ s on two architectures;2) the offsets are less than that of LinuxPTP and Chrony;and 3) the vmPTP's CPU and memory utilization on the X86-64 architecture are merely 0.3% and 0.1%, respectively, whereas those on the ARM64 architecture are 3.8% and 0.1%, respectively. We experiment with vmPTP in real-time application and the result demonstrates th

关键词： Linux

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Brief Industry Paper: Response Time Evaluation of Cross-Domain Communication in CAN-FD and TSN 44

Brief Industry Paper: Response Time Evaluation of Cross-Doma...

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44th IEEE Real-Time Systems Symposium, RTSS 2023

作者： Ma, Wenhong Huang, Xiaoyi Wei, Dongsheng Li, Renfa Zhang, Yunfei Xie, Guoqi Chang, Wanli Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province China Tencent Future Network Lab Tencent Corporation China

ISBN: (纸本)9798350328578

With the advancement of intelligence and networked automotive, the domain-centralized architecture, which employs time sensitive networking (TSN) as the inter-domain backbone network and control area network with flexible data rate (CAN-FD) as the intra-domain network, has garnered significant attention. However, cross-domain end-to-end communication involves multiple components, and significant disparities between TSN and CAN-FD render response time analysis within domain-centralized architecture for mixed-critical traffic exceptionally complex. In this paper, we develop a cross-domain with TSN and CAN-FD end-to-end response time evaluation tool, which analyzes the response time of mixed-critical traffic under different design options segment by segment. We specifically analyze the waiting times of different messages in the domain control unit when faced with the design options of one-to-one and multi-to-one conversion of CAN-FD and TSN frames. The proposed evaluation tool can be easily extended to different design options to support more application scenarios. Theoretical computational analysis and real hardware measurements show the effectiveness of our tool. © 2023 IEEE.

关键词： network architecture

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Locally differentially private high-dimensional data synthesis

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Science China(Information Sciences) 2023年第1期66卷 25-42页

作者： Xue CHEN Cheng WANG Qing YANG Teng HU Changjun JIANG Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

In local differential privacy(LDP), a challenging problem is the ability to generate highdimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensional data synthesis, which partition the privacy budget among all attributes, cease to be effective in high-dimensional scenarios due to the large-scale noise and communication cost caused by the high dimension. In fact, the high-dimensional characteristics not only bring challenges but also make it possible to apply some technologies to break this bottleneck. This paper presents Sam Priv Syn for high-dimensional data synthesis under LDP, which is composed of a marginal sampling module and a data generation *** marginal sampling module is used to sample from the original data to obtain two-way marginals. The sampling process is based on mutual information, which is updated iteratively to retain, as much as possible,the correlation between attributes. The data generation module is used to reconstruct the synthetic dataset from the sampled two-way marginals. Furthermore, this study conducted comparison experiments on the real-world datasets to demonstrate the effectiveness and efficiency of the proposed method, with results proving that Sam PrivSyn can not only protect privacy but also retain the correlation information between the attributes.

关键词： local differential privacy high-dimensional data synthesis sampling data privacy privacy-preserving protocols

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Instance-Aware Style-Swap for Disentangled Attribute-Level Image Editing 17th

Instance-Aware Style-Swap for Disentangled Attribute-Level ...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Zhou, Xinjiao Jiang, Bin Yang, Chao Hu, Haotian Sun, Minyu College of Computer Science and Electronic Engineering Hunan University Hunan Changsha410082 China Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Hunan Changsha410082 China

ISBN: (纸本)9789819923847

Recent studies have shown that attribute-level image editing could be achieved by modifying the latent code of style-based Generative Adversarial network (StyleGAN). However, many existing methods suffer from semantic entanglement, which leads to undesirable changes when editing a specific attribute. To solve this problem, we focus on the S space of StyleGAN2 and propose a simple and effective Optimization-based Instance-Aware Style-Swap method, which could perform a natural transformation effect on arbitrary source images and reference images by searching instance-aware swap coefficients with the guidance of prior information. By further analyzing the learned swapping coefficients, we can identify the style channels connected with a specific target attribute. We verify the effectiveness of our proposed methods in the field of face attribute editing. Extensive experiments have demonstrated that our method can achieve controllable and fine-grained image editing along various attributes. Qualitative and quantitative results show the advantages of our method compared with other semantic image editing methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Collaborative Multi-head Contextualized Sparse Representations for Real-Time Open-Domain Question Answering 17th

Collaborative Multi-head Contextualized Sparse Representatio...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Sun, Minyu Jiang, Bin Zhou, Xinjiao Zhang, Bolin Yang, Chao College of Computer Science and Electronic Engineering Hunan University Hunan Changsha410082 China Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Hunan Changsha410082 China

ISBN: (纸本)9789819923847

An efficient method of representing and retrieving information is an essential component of open domain QA. There are question and answer models that allow for real-time responses with speed benefit and scalability. Nonetheless, due to the limitations of existing phrase models, their accuracy is low. In this paper, we improve the contextualized sparse representation to strengthen the connection between contextual information. We achieve better answer retrieval by enhancing the embedding quality of the model for phrase representation. Specifically, based on original contextualized sparse representations, we transform the single self-attention into collaborative multi-head attention so that attention heads can connect and pay attention to crucial information in different context locations. Compared with learning sparse vectors in n-gram vocabulary space by rectified self-attention, collaborative multi-head attention performs better on the SQuAD dataset. Due to the increased efficiency of critical information representation, the model improves to varying degrees on both of the two evaluation metrics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vector spaces

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Diverse Image Colorizaiton via Deep Color Prior and Triplet Latent Regularization

Diverse Image Colorizaiton via Deep Color Prior and Triplet ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Jiang, Bin Dai, Jiawu Zeng, Weiyuan Wang, Renjun College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Automatic image colorization has made tremendous progress in recent years. However, previous methods rarely consider the inherent diversity of natural images, and existing diverse colorization approaches still suffer from the following two limitations. i) The diversity of generated images is limited. ii) Some inevitable artifacts substantially degrade the quality of colorization results. To cope with such problems, we propose a novel diverse image colorization network based on vanilla GAN, which extracts the deep color priors through an initial colorization network first, and then utilizes these priors to modulate the diverse generation to achieve high-fidelity colorization outputs. In addition, a novel triplet latent regularization is proposed to constrain the correspondence between latent codes and generated images, which efficiently alleviates mode collapse and encourages more diverse colorization results. Extensive experiments on three benchmark datasets demonstrate the superiority of our method over existing diverse image colorization models. © 2022 IEEE.

关键词： Generative adversarial networks

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A perspective on Petri Net learning

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Frontiers of Computer Science 2023年第6期17卷 1-3页

作者： Hongda QI Changjun JIANG Key Laboratory of Embedded System and Service Computing(Ministry of Education) Tongji UniversityShanghai 201804China National(Province-Ministry Joint)Collaborative Innovation Center for Financial Network Security Tongji UniversityShanghai 201804China

Petri Nets(PNs)are used for modeling and analyzing discreteevent systems,such as communication protocols,trafficsystems,human-computer interaction,and fault ***’state space explosion problem means that the state spaceof PNs grows exponentially with PNs’*** thefundamental reachability problem is still an NP-Hard problemin *** has been proved that the equivalence problem forthe reachability sets of arbitrary PNs is undecidable except forsome subclass of PNs[1].That is,the reachability problem ofarbitrary PNs cannot be solved ***,there is noefficient and accurate algorithm to solve the problem.10172In recent years,with the emergence of big data and thedevelopment of computing hardware,a series ofbreakthroughs have been achieved in machine learning,suchas AlphaGo,AlphaFold,and ChatGPT[2−4].As a data-drivenapproach,machine learning can learn potential mappingrelationships between inputs and outputs from large-scaledata.

关键词： breakthrough hardware learning

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TrafficGPT: Towards Multi-Scale Traffic Analysis and Generation with Spatial-Temporal Agent Framework

arXiv

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arXiv 2024年

作者： Ouyang, Jinhui Zhu, Yijie Yuan, Xiang Wu, Di Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha Hunan410082 China

The precise prediction of multi-scale traffic is a ubiquitous challenge in the urbanization process for car owners, road administrators, and governments. In the case of complex road networks, current and past traffic information from both upstream and downstream roads are crucial since various road networks have different semantic information about traffic. Rationalizing the utilization of semantic information can realize short-term, long-term, and unseen road traffic prediction. As the demands of multi-scale traffic analysis increase, on-demand interactions and visualizations are expected to be available for transportation participants. We have designed a multi-scale traffic generation system, namely TrafficGPT, using three AI agents to process multi-scale traffic data, conduct multi-scale traffic analysis, and present multi-scale visualization results. TrafficGPT consists of three essential AI agents: 1) a text-todemand agent that is employed with Question & Answer AI to interact with users and extract prediction tasks through texts;2) a traffic prediction agent that leverages multi-scale traffic data to generate temporal features and similarity, and fuse them with limited spatial features and similarity, to achieve accurate prediction of three tasks;and 3) a suggestion and visualization agent that uses the prediction results to generate suggestions and visualizations, providing users with a comprehensive understanding of traffic conditions. Our TrafficGPT system focuses on addressing concerns about traffic prediction from transportation participants, and conducted extensive experiments on five realworld road datasets to demonstrate its superior predictive and interactive performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Visualization

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A Novel Neighborhood-Weighted Sampling Method for Imbalanced Datasets

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Chinese Journal of Electronics 2022年第5期31卷 969-979页

作者： GUANG Mingjian YAN Chungang LIU Guanjun WANG Junli JIANG Changjun Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm's performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier's diversity. We compare NWBBagging with some state-of-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.

关键词： parameter reduction method neighborhood-weighted sampling method pattern classification class imbalance problem NWBBagging k-nearest neighbors sampling methods imbalanced datasets nearest neighbour methods ensemble learning algorithm Bagging algorithm

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Cyber-Physical Systems Design in An Uncertain Environment with Time Uncertainty Concern 29

Cyber-Physical Systems Design in An Uncertain Environment wi...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Chen, Si Huang, Lida Xiao, Xiongren Liu, Yan Xie, Guoqi Li, Renfa Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Changsha China Research Institute of Hunan University in Chongqing Center for Convergence of Automobile and Cyberspace Chongqing China

ISBN: (纸本)9798350330717

Multiple processors system on chip (MPSoC) has been the trend in cyber-physical systems (CPSs), and reasonable partitioning for MPSoC resources is a critical step in CPSs design. The uncertainties of environment and time are both important factors that need to be considered in the design, but none of the previous work pays attention to two uncertainties at the same time. The state-of-the-art work presented a detailed process of applying uncertain programming to solve the partitioning problem, which provides a solution for designing in an uncertain environment. However, this work only considers the bipartition scenario which cannot be directly applied to MPSoC, and it does not focus specifically on time uncertainty. In this paper, we propose a method for modeling the MPSoC partitioning problem in an uncertain environment, with the time uncertainty concern. We present the uncertain model that can be applied to the multiple optional resources scenario. We build the optimization model with the objective of minimizing time, analyze two different cases of minimizing the uncertain time, and finally prove a unified deterministic model to solve. We come up with three algorithms, including the heuristic algorithm, the genetic algorithm, and the exact algorithm, and experiments show that the heuristic algorithm and the genetic algorithm can obtain good approximate solutions compared with the exact algorithm. © 2023 IEEE.

关键词： Cyber-physical systems (CPSs) design multiple processors system on chip (MPSoC) partitioning time uncertainty uncertain environment

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