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IEEE International Conference on Joint Cloud computing (JCC)

作者： Yaojie Li Peichang Shi Jianfei Liu Rui Li Fei Gao Penghui Ma Dong Xie Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users’ actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud *** address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

关键词： Performance evaluation Adaptation models Costs Scheduling algorithms Scalability Serverless computing Collaboration

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A consistency algorithm for causal and totally ordered event delivery in DVE

A consistency algorithm for causal and totally ordered event...

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International Conference on computer Science and Information Technology (CSIT)

作者： Hangjun Zhou Wei Zhang Yuxing Peng Sikun Li Department of Information Management Hunan College of Finance and Economics Changsha Hunan China Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan China

How to preserve causal and totally ordered event delivery is an important issue in real-time serverless DVE(distributed Virtual Environment). However, most of the related works are designed to maintain causal order merely or time stamped order with intensive computation and bandwidth overhead. In this paper, we proposed a novel distributed algorithm to maintain the before-and-after relationship between events, both causal and concurrent, of DVE at each individual node. Several simulation experiments are carried out to evaluate the performance of our algorithm and the results demonstrate that the algorithm is effective in preserving causal and totally ordered event delivery and more efficient than the previous algorithms.

关键词： Artificial neural networks Silicon Educational institutions

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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International Conference on Data Engineering

作者： Junhui Gao Qianru Wang Xin Zhang Juan Shi Xiang Zhao Qingye Han Yan Pan School of Computer Science Northwestern Polytechnical University School of Computer Science and Technology Xidian University National Key Laboratory of Information Systems Engineering National University of Defense Technology Air Force Engineering University Laboratory for Big Data and Decision National University of Defense Technology School of Management Science and Real Estate Chongqing University National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery.

关键词： Autonomous aerial vehicles Data engineering Air to ground communication Trajectory Batteries Time factors Task analysis

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Feature and Performance Comparison of FaaS Platforms

Feature and Performance Comparison of FaaS Platforms

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Penghui Ma Peichang Shi Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.

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Enhancing 2D Representation via Adjacent Views for 3D Shape Retrieval

Enhancing 2D Representation via Adjacent Views for 3D Shape ...

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International Conference on computer Vision (ICCV)

作者： Cheng Xu Zhaoqun Li Qiang Qiu Biao Leng Jingfei Jiang School of Computer Science & Engineering Beihang University Duke University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, CenterNet, to enhance each individual 2D view using its neighboring ones. By exploiting cross-view correlations, CenterNet learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for a panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNetCore55 benchmarks, and also exhibits robustness against object occlusion.

关键词： Shape Three-dimensional displays Correlation Two dimensional displays Feature extraction Solid modeling Computational modeling

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Collaborative deep learning across multiple data centers

arXiv

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arXiv 2018年

作者： Xu, Kele Mi, Haibo Feng, Dawei Wang, Huaimin Chen, Chuan Zheng, Zibin Lan, Xu National Key Laboratory of Parallel and Distributed Processing Changsha China College of Computer National University of Defense Technology Changsha China School of Data and Computer Science Sun Yat-Sen University Guangzhou China Queen Mary University of London London United Kingdom

Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data to a centralized data center due to not only bandwidth limitation but also the constraints of privacy regulations. Model averaging is a conventional choice for data parallelized training, but its ineffectiveness is claimed by previous studies as deep neural networks are often non-convex. In this paper, we argue that model averaging can be effective in the decentralized environment by using two strategies, namely, the cyclical learning rate and the increased number of epochs for local model training. With the two strategies, we show that model averaging can provide competitive performance in the decentralized mode compared to the data-centralized one. In a practical environment with multiple data centers, we conduct extensive experiments using state-of-the-art deep network architectures on different types of data. Results demonstrate the effectiveness and robustness of the proposed method. Copyright © 2018, The Authors. All rights reserved.

关键词： Network architecture

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A Physics and Data-Driven Hybrid PINNs Intelligent computing Method for Nuclear Engineering Simulation

A Physics and Data-Driven Hybrid PINNs Intelligent Computing...

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Electronic Information Engineering and computer Science (EIECS), 2021 International Conference on

作者： Yufei Xie Wenlin Wang Guohua Wu Yang Yu Ping An Zibin Sun Haichuan Zhang Shengfeng Luo Yue Li School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Nuclear Power Institute of China Chengdu China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In the field of nuclear energy, the Loss of Coolant Accident (LOCA) is recognized as one of the most severe types of nuclear reactor accidents, characterized by its complex physical processes and potentially catastrophic consequences. These challenges impose stringent requirements on safety analysis and emergency response. Accurate prediction and analysis of fluid behavior within pipelines under LOCA conditions are critical for evaluating accident outcomes and formulating response strategies. This paper introduces an innovative intelligent computing approach—a Physics-Informed Neural Networks (PINNs) model driven by both physical data and simulation data, specifically tailored for LOCA conditions. To address the challenges posed by multivariable and complex physical relationships, the six-equation two-fluid model is first simplified to represent the physical processes. Subsequently, a dual-driven PINNs network is developed, integrating both simulation data and physical constraints. The proposed model demonstrates a root mean square error (RMSE) of 0.02, a mean absolute error (MAE) of 0.044, and an R2 of 0.81 in predicting the outcomes under the six-equation model.

关键词： Solid modeling Accuracy Fluids Nuclear energy Computational modeling Pipelines Neural networks Predictive models Numerical models Accidents

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科学研究的第五范式——以智能驱动的材料设计为例

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Engineering 2023年第5期24卷 126-137,I0003,I0004页

作者： Can Leng Zhuo Tang Yi-Ge Zhou Zean Tian Wei-Qing Huang Jie Liu Keqin Li Kenli Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China National Supercomputing Center in Changsha Changsha 410082China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Institute of Chemical Biology and Nanomedicine State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringHunan UniversityChangsha 410082China Department of Applied Physics School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Computer Science State University of New YorkNew PaltzNY 12561USA

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地... 详细信息

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地阐明了第五范式的本质,旨在促进第五范式在其他领域的培养。第五范式平台主要包括模型自动构建(原始数据提取)、指纹自动构建(神经网络特征选择)以及跨学科知识串联的重复迭代(“火山图”)。与分解一起进行的是对迭代中实现的体系结构的性能评估。通过讨论,第五范式的智能驱动平台可以极大地简化和改进研究中极其繁琐和具有挑战性的工作,并通过补偿机器学习中缺少样本和替代一些由于计算资源不足而导致的数值计算来实现数值计算与机器学习之间的相互反馈,从而加速探索过程。在数据驱动的学科中,跨学科专家的协同作用和对动态数据需求的急剧增长仍然是一个挑战。我们相信,对第五范式平台的一瞥可以为其在其他领域的应用铺平道路。

关键词：机器学习自动构建天河一号数据驱动神经网络动态数据知识整合科学研究

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A Retina-LGN-V1 Structure-like Spiking Neuron Network for Image Feature Extraction 21

A Retina-LGN-V1 Structure-like Spiking Neuron Network for Im...

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Proceedings of the 2021 5th International Conference on Video and Image Processing

作者： Sheng Ji Qijun Gu Ye Yuan Peng Zhao Tao Fang Hong Huo Xin Niu Department of Automation_School of Electronic Information and Electrical Engineering_Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China College of Computer Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (纸本)9781450385893

SNNs have achieved great attention in recent years as they contain neurons more like those in the brain and use spikes to encode and transmit information efficiently among neurons with lower energy consumption. A Retina-LGN-V1 structure-like spiking neuron network (RLVSL-SNN) is proposed in this paper. It is inspired by the structure of mammalian primary visual pathway, and simulates different biological structures of Retina, LGN and V1. Noise reduction circuit and light adaptation circuit are also simulated for enhancing the robustness of its extracted features. RLVSL-SNN is a bio-plausible neuron network as it has firing rates of neurons in each layer that are similar to those of biological experiments. Besides, a full-connected SNN (FC SNN) is implemented following RLVSL-SNN for classification to evaluate the extracted features. The additive spiking timing dependent plasticity (STDP) learning rules and the ANN-to-SNN conversion method are utilized to train RLVSL-SNN and FC SNN, respectively. The experiments on MNIST dataset have verified that RLVSL-SNN is comparable to AlexNet for classification by features from spikes.

关键词： Additive Spiking Timing Dependent Plasticity Learning Primary Visual Pathway Image Classification Spiking Neural Network

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Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons

arXiv

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arXiv 2022年

作者： Jin, Haibo Chen, Ruoxi Zheng, Haibin Chen, Jinyin Cheng, Yao Yu, Yue Liu, Xianglong College of Information Engineering Zhejiang University of Technology Hangzhou China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China Huawei International Singapore National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China State Key Laboratory of Software Development Environment Beihang University Beijing China

Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequently occurred erroneous behaviors. Therefore, it is necessary to conduct a systematical testing for DNNs before they are deployed to real-world applications. Existing testing methods have provided fine-grained metrics based on neuron coverage and proposed various approaches to improve such metrics. However, it has been gradually realized that a higher neuron coverage does not necessarily represent better capabilities in identifying defects that lead to errors. Besides, coverage-guided methods cannot hunt errors due to faulty training procedure. So the robustness improvement of DNNs via retraining by these testing examples are unsatisfactory. To address this challenge, we introduce the concept of excitable neurons based on Shapley value and design a novel white-box testing framework for DNNs, namely DeepSensor. It is motivated by our observation that neurons with larger responsibility towards model loss changes due to small perturbations are more likely related to incorrect corner cases due to potential defects. By maximizing the number of excitable neurons concerning various wrong behaviors of models, DeepSensor can generate testing examples that effectively trigger more errors due to adversarial inputs, polluted data and incomplete training. Extensive experiments implemented on both image classification models and speaker recognition models have demonstrated the superiority of DeepSensor. Compared with the state-of-the-art testing approaches, DeepSensor can find more test errors due to adversarial inputs (∼ ×1.2), polluted data (∼ ×5) and incompletely-trained DNNs (∼ ×1.3). Additionally, it can help DNNs build larger l2-norm robustness bound (∼ ×3) via retraining according to CLEVER's certification. We further provide interpretable proofs for effectiveness of DeepSensor via excitable neuro

关键词： Deep neural networks

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