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Predictable bandwidth slicing with open vSwitch

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arXiv 2021年

作者： Chen, Jesse Dezfouli, Behnam Internet of Things Research Lab Department of Computer Science and Engineering Santa Clara University United States

Software switching, a.k.a virtual switching, plays a vital role in network virtualization and network function virtualization, enhances configurability, and reduces deployment and operational costs. Software switching also facilitates the development of edge and fog computing networks by allowing the use of commodity hardware for both data processing and packet switching. Despite these benefits, characterizing and ensuring deterministic performance with software switches is harder, compared to physical switching appliances. In particular, achieving deterministic performance is essential to adopt software switching in mission-critical applications, especially those deployed in edge and fog computing architectures. In this paper, we study the impact of switch configurations on bandwidth slicing and predictable packet latency. We demonstrate that latency and predictability are dependent on the implementation of the bandwidth slicing mechanism and that the packet schedulers used in OVS Kernel-Path and OVS-DPDK each focus on different aspects of switching performance. © 2021, CC BY.

关键词： Fog computing

Modeling Crowdedness of Emergency departments Leveraging Crowdsensing Mobility Data 19

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Modeling Crowdedness of Emergency Departments Leveraging Cro...

2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Shou, Tieqi Wang, Zhiyuan Shi, Shang Yang, Dingqi Zhou, Binbin Wang, Cheng Chen, Longbiao Xiamen University SpAtial Sensing and Computing Lab Xiamen China University of Virginia School of Engineering and Applied Science Charlottesville United States Imperial College London Department of Mathematics London United Kingdom University of Macau State Key Laboratory of Internet of Things for Smart City China Zhejiang University City College Department of Computer Science and Computing Hangzhou China

ISBN: (纸本)9798350346558

Hospital emergency departments (EDs) are crucial medical facilities providing emergency healthcare. Understanding and measuring the patient flow in EDs plays a key role in maximizing the utility of scarce hospital resources. Existing approaches either require manual reporting by staff or collect sensitive data about patients via camera or hospital registration system, causing potential privacy breaches. Against this background, we propose a data-driven modeling strategy of ED crowdedness leveraging multi-source urban crowdsensing data, which automatically provides fine-grained and timely information about the crowdedness of EDs. Specifically, our model can not only accurately extract the emergency visit demand from noisy human mobility data with minimum expert knowledge using active learning and co-training techniques, but also estimate ED crowdedness by modeling the emergency service process by integrating three queueing models for general practice, internal medicine, and surgical clinics, respectively. We evaluate our method leveraging large-scale taxi and hire vehicle trajectory datasets and hospital information system datasets from the government open data portal. Results show that our approach effectively extracts the emergency visit demand and models the emergency service process to assess the crowdedness of EDs. We have deployed the system in Xiamen City by collaborating with the municipal government to provide services for citizens and health providers. © 2022 IEEE.

关键词： Emergency services

Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical Perspective

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arXiv 2025年

作者： Yao, Yuan Zhang, Xiaopu Zhang, Yu Jin, Jian Yang, Qiang Beijing Teleinfo Technology Company Ltd. China Academy of Information and Communications Technology Beijing100095 China Department of Research and Development Inspur Computer Technology Co. Ltd. Beijing100095 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Industrial Internet of Things China Academy of Information and Communications Technology Beijing100095 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong WeBank Shenzhen518052 China

Semi-supervised heterogeneous domain adaptation (SHDA) addresses learning across domains with distinct feature representations and distributions, where source samples are labeled while most target samples are unlabeled, with only a small fraction labeled. Moreover, there is no one-to-one correspondence between source and target samples. Although various SHDA methods have been developed to tackle this problem, the nature of the knowledge transferred across heterogeneous domains remains unclear. This paper delves into this question from an empirical perspective. We conduct extensive experiments on about 330 SHDA tasks, employing two supervised learning methods and seven representative SHDA methods. Surprisingly, our observations indicate that both the category and feature information of source samples do not significantly impact the performance of the target domain. Additionally, noise drawn from simple distributions, when used as source samples, may contain transferable knowledge. Based on this insight, we perform a series of experiments to uncover the underlying principles of transferable knowledge in SHDA. Specifically, we design a unified Knowledge Transfer Framework (KTF) for SHDA. Based on the KTF, we find that the transferable knowledge in SHDA primarily stems from the transferability and discriminability of the source domain. Consequently, ensuring those properties in source samples, regardless of their origin (e.g., image, text, noise), can enhance the effectiveness of knowledge transfer in SHDA tasks. The codes and datasets are available at https://***/yyyaoyuan/SHDA. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

Two-Stage Resource Scheduling for Deterministic Communication and Computation Integration

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Two-Stage Resource Scheduling for Deterministic Communicatio...

IEEE Conference on Global Communications (GLOBECOM)

作者： Weiting Zhang Nian Tang Chuan Zhang Ruibin Guo Mingyan Li Chenhao Ying Jian Jin School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China College of Computer Science Chongqing University Chongqing China Department of Computer Science Shanghai Jiao Tong University Shanghai China Research Institute of Industrial Internet of Things CAICT Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, we investigate a resource orchestration and transmission scheduling problem for data-intensive services with diversified service requirements. A three-layer collaborative architecture is presented to support dynamic networking and computing resource allocation. To obtain optimal orchestration and scheduling policies, we formulate a constrained resource scheduling problem with the objective to maximizing resource utilization and scheduling success ratio. Since the complicated coupled constraints among decisions, we decouple the problem into a two-stage sub-problems of resource orchestration and transmission scheduling. To realize cross-domain resource orchestration and deterministic transmission of large-scale computing tasks, a two-stage resource scheduling scheme is proposed. Specifically, the first stage makes the resource orchestration decision by a greedy algorithm, and the second stage makes the transmission scheduling decision based on a deep reinforcement learning algorithm. Simulation results show that the proposed solution can effectively improve resource utilization and scheduling success ratio while satisfying diversified service requirements, as compared with benchmarks.

关键词： Greedy algorithms Processor scheduling Simulation Heuristic algorithms Design methodology Benchmark testing Dynamic scheduling Deep reinforcement learning Resource management Global communication

Expeditious Diagnosis of Breast Cancer from Magnetic Resonance Images Using Grey Wolf Optimization based Improved K-means Algorithm

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Expeditious Diagnosis of Breast Cancer from Magnetic Resonan...

Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： Krishna Narayanan S Sundar Ganesh C S Petchinathan G Kottaimalai Ramaraj Pallikonda Rajasekaran Murugan Thilagaraj M Department of Computer Science and Engineering MVJ College of Engineering Bengaluru India Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Tamilnadu India Department of Biomedical Engineering Sri Shanmugha College of Engineering and Technology Tiruchengode Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Industrial Internet of Things MVJ College of Engineering Bengaluru India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

Considering early recognition can improve patient survival rates, it is one of the most crucial concerns that ought to be addressed globally. Promptly identifying any malignant cell or cells has the potential to prevent millions of deaths. Early detection of breast cancer has been achieved by the use of MRIs and mammograms; if the disease is found early, the cost of therapy can be significantly lowered. Methods for segmentation are necessary for the identification of breast cancers. In analyzing images, segmentation—which comprises identification, extraction of features, categorization, and treatment—plays a crucial role. In order to plan treatments, segmentation assists doctors in measuring the amount of tissue in the breast. Utilizing image classification techniques, one may determine the degree of cancer by analyzing the form of the malignant cells. The application of unsupervised K-Means clustering is employed to distinguish the region of interest from the background. By utilizing K-centroids, the data is segmented into K-clusters. To enhance segmentation outcomes, the Grey Wolf Optimization (GWO) is integrated with K-means. GWO is a contemporary metaheuristic swarm intelligence technique known for its notable advantages over traditional methods. Notably, it excels in the absence of derivative information during the initial search and involves minimal parameters. As a result, GWO is widely utilized for optimizing various ***, it is straightforward, user-friendly, adaptable, scalable, and has the unique ability to manage exploration and exploitation throughout the search in a way that promotes optimal convergence. When tested on the MIAS breast cancer dataset, the hybrid GWO based enhanced K-means (GWO_iK-means) algorithm scored 93% dice score value on segmenting the infected region.

关键词： Image segmentation Technological innovation Data science Feature extraction Search problems Breast cancer Artificial intelligence Medical diagnostic imaging Standards Strain

DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV

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IEEE Transactions on Green Communications and Networking

IEEE Transactions on Green Communications and Networking 2025年

作者： Zhang, Zheng Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Letaief, Khaled B. Jiangnan University School of Internet of Things Engineering Wuxi214122 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering State Key laboratory of Space Network and Communications Tsinghua University Beijing100084 China Xidian University State Key Lab. of ISN School of Telecom-munications Engineering Xi'an710071 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Department of Electrical and Computer Engineer-ing Hong Kong

To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI. © 2017 IEEE.

关键词： Deep reinforcement learning

Enhanced SPS Velocity-Adaptive Scheme: Access Fairness in 5G NR V2I Networks

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arXiv 2025年

作者： Xu, Xiao Wu, Qiong Fan, Pingyi Wang, Kezhi School of Internet of Things Engineering Jiangnan University Wuxi214122 China Department of Electronic Engineering State Key laboratory of Space Network and Communications Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Computer Science Brunel University Middlesex LondonUB8 3PH United Kingdom

Vehicle-to-Infrastructure (V2I) technology enables information exchange between vehicles and road infrastructure. Specifically, when a vehicle approaches a roadside unit (RSU), it can exchange information with the RSU to obtain accurate data that assists in driving. As the 3rd Generation Partnership Project (3GPP) Release 16, which includes the 5G New Radio (NR) Vehicle-to-Everything (V2X) standards, vehicles typically adopt mode-2 communication using sensing-based semi-persistent scheduling (SPS). By this approach, vehicles identify resources through a selection window and exclude ineligible resources based on information from a sensing window. However, vehicles often drive at different speeds, resulting in varying amounts of data transmission with RSUs as they pass by, which leads to unfair access. Therefore, developing an scheme that accounts for different vehicle speeds to achieve fair access across the network is essential. This paper formulates an optimization problem for vehicular environment and proposes a multi-objective optimization scheme to address it by adjusting the selection window size. Experimental results validate the efficiency of the proposed method. © 2025, CC BY-NC-SA.

关键词： 5G mobile communication systems

Rethinking guidance information to utilize unlabeled samples: a label encoding perspective 24

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Rethinking guidance information to utilize unlabeled samples...

Proceedings of the 41st International Conference on Machine Learning

作者： Yulong Zhang Yuan Yao Shuhao Chen Pengrong Jin Yu Zhang Jian Jin Jiangang Lu Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China and College of Control Science and Engineering Zhejiang University Hangzhou China Beijing Teleinfo Technology Company Ltd. China Academy of Information and Communications Technology Beijing China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Research Institute of Industrial Internet of Things China Academy of Information and Communications Technology Beijing China College of Control Science and Engineering Zhejiang University Hangzhou China

Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples. A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their learning. However, EntMin emphasizes prediction discriminability while neglecting prediction diversity. To alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled samples. By analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label encoding. Inspired by this finding, we propose a label-Encoding Risk Minimization (LERM). It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label encodings. As a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a plugin. Theoretically, we analyze the relationships between LERM and ERM as well as EntMin. Empirically, we verify the superiority of the LERM under several label insufficient scenarios. The codes are available at https://***/zhangyl660/LERM.

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Rethinking Guidance Information to Utilize Unlabeled Samples: A label Encoding Perspective

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arXiv 2024年

作者： Zhang, Yulong Yao, Yuan Chen, Shuhao Jin, Pengrong Zhang, Yu Jin, Jian Lu, Jiangang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China College of Control Science and Engineering Zhejiang University Hangzhou China Beijing Teleinfo Technology Company Ltd. China Academy of Information and Communications Technology Beijing China Research Institute of Industrial Internet of Things China Academy of Information and Communications Technology Beijing China

关键词： Forecasting