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Scheduling Multi-Server Jobs with Sublinear Regrets via Online Learning

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arXiv 2023年

作者： Zhao, Hailiang Deng, Shuiguang Xiang, Zhengzhe Yan, Xueqiang Yin, Jianwei Dustdar, Schahram Zomaya, Albert Y. Hainan Institute of Zhejiang University Sanya572025 China The College of Computer Science and Technology Zhejiang University Hangzhou310027 China The School of Computer Science and Technology Hangzhou City University Hangzhou310015 China Huawei Technologies Co Ltd Shanghai201206 China The Distributed Systems Group Technische Universität Wien Vienna1040 Austria The School of Computer Science University of Sydney SydneyNSW2006 Australia

Multi-server jobs that request multiple computing resources and hold onto them during their execution dominate modern computing clusters. When allocating the multi-type resources to several co-located multi-server jobs simultaneously in online settings, it is difficult to make the tradeoff between the parallel computation gain and the internal communication overhead, apart from the resource contention between jobs. To study the computation-communication tradeoff, we model the computation gain as the speedup on the job completion time when it is executed in parallelism on multiple computing instances, and fit it with utilities of different concavities. Meanwhile, we take the dominant communication overhead as the penalty to be subtracted. To achieve a better gain-overhead tradeoff, we formulate an cumulative reward maximization program and design an online algorithm, named OGASCHED, to schedule multi-server jobs. OGASCHED allocates the multi-type resources to each arrived job in the ascending direction of the reward gradients. It has several parallel sub-procedures to accelerate its computation, which greatly reduces the complexity. We proved that it has a sublinear regret with general concave rewards. We also conduct extensive trace-driven simulations to validate the performance of OGASCHED. The results demonstrate that OGASCHED outperforms widely used heuristics by 11.33%, 7.75%, 13.89%, and 13.44%, respectively. © 2023, CC BY-NC-ND.

关键词： E-learning

Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data

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arXiv 2023年

作者： Monninger, Thomas Weber, Andreas Staab, Steffen Mercedes-Benz Research & Development North America SunnyvaleCA United States Mercedes-Benz AG Research & Development Stuttgart Germany University of Stuttgart Institute of Parallel and Distributed Systems Stuttgart Germany University of Southampton Electronics and Computer Science Southampton United Kingdom

Understanding which traffic light controls which lane is crucial to navigate intersections safely. Autonomous vehicles commonly rely on High Definition (HD) maps that contain information about the assignment of traffic lights to lanes. The manual provisioning of this information is tedious, expensive, and not scalable. To remedy these issues, our novel approach derives the assignments from traffic light states and the corresponding motion patterns of vehicle traffic. This works in an automated way and independently of the geometric arrangement. We show the effectiveness of basic statistical approaches for this task by implementing and evaluating a pattern-based contribution method. In addition, our novel rejection method includes accompanying safety considerations by leveraging statistical hypothesis testing. Finally, we propose a dataset transformation to re-purpose available motion prediction datasets for semantic map learning. Our publicly available API for the Lyft Level 5 dataset enables researchers to develop and evaluate their own approaches5 Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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

Image enhancement in embedded devices for internet of things

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Image enhancement in embedded devices for internet of things

作者： Jeon, Gwanggil Pasupa, Kitsuchart Anisetti, Marco Ahmad, Awais Department of Embedded Systems Engineering Incheon National University Incheon22012 Korea Republic of Faculty of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok10520 Thailand 20133 Italy Department of Computer Science Bahria University Islamabad Pakistan

This paper proposes a new color interpolation method which can be used in embedded devices for IoT system. In this work, we use regression approach for generating and designing filters to restore color image. The filters are designed with four sizes, 5x5 training filter, 7x7 training filter, 9x9 training filter, and 11x11 training filter. The obtained filters are tested in 25 LC dataset to assess the performance. Experimental results inform that the proposed filters provide outstanding performance when they are compared with conventional methods. As compared with the other methods, the proposed filters produce the best average interpolation performance both objectively and visually. © 2019 John Wiley & Sons, Ltd.

关键词： Image enhancement

FEniCS-preCICE: Coupling FEniCS to other simulation software

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arXiv 2021年

作者： Rodenberg, Benjamin Desai, Ishaan Hertrich, Richard Jaust, Alexander Uekermann, Benjamin Scientific Computing in Computer Science Department of Informatics Technical University of Munich Germany Usability and Sustainability of Simulation Software Institute for Parallel and Distributed Systems University of Stuttgart Germany Simulation of Large Systems Institute for Parallel and Distributed Systems University of Stuttgart Germany

The new software FEniCS-preCICE is a middle software layer, sitting in between the existing finite-element library FEniCS and the coupling library preCICE. The middle layer simplifies coupling (existing) FEniCS application codes to other simulation software via preCICE. To this end, FEniCS-preCICE converts between FEniCS and preCICE mesh and data structures, provides easy-to-use coupling conditions, and manages data checkpointing for implicit coupling. The new software is a library itself and follows a FEniCS-native style. Only a few lines of additional code are necessary to prepare a FEniCS application code for coupling. We illustrate the functionality of FEniCS-preCICE by two examples: a FEniCS heat conduction code coupled to OpenFOAM and a FEniCS linear elasticity code coupled to SU2. The results of both scenarios are compared with other simulation software showing good agreement. © 2021, CC BY-NC-ND.

关键词： Finite element method

A Review of Techniques for Ageing Detection and Monitoring on embedded systems

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arXiv 2023年

作者： Lanzieri, Leandro Martino, Gianluca Fey, Goerschwin Schlarb, Holger Schmidt, Thomas C. Wählisch, Matthias Deutsches Elektronen-Synchrotron DESY HAW Hamburg Germany Institute of Embedded Systems Hamburg University of Technology Hamburg Germany Deutsches Elektronen-Synchrotron DESY Hamburg Germany Department Informatik HAW Hamburg Hamburg Germany Faculty of Computer Science TU Dresden and Barkhausen Institut Dresden Germany

embedded digital devices are progressively deployed in dependable or safety-critical systems. These devices undergo significant hardware ageing, particularly in harsh environments. This increases their likelihood of failure. It is crucial to understand ageing processes and to detect hardware degradation early for guaranteeing system dependability. In this survey, we review the core ageing mechanisms, identify and categorize general working principles of ageing detection and monitoring techniques for Commercial-Off-The-Shelf (COTS) components that are prevalent in embedded systems: Field Programmable Gate Arrays (FPGAs), microcontrollers, System-on-Chips (SoCs), and their power supplies. From our review, we find that online techniques are more widely applied on FPGAs than on other components, and see a rising trend towards machine learning application for analysing hardware ageing. Based on the reviewed literature, we identify research opportunities and potential directions of interest in the field. With this work, we intend to facilitate future research by systematically presenting all main approaches in a concise way. Copyright © 2023, The Authors. All rights reserved.

关键词： Microcontrollers

SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks

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arXiv 2023年

作者： Monninger, Thomas Schmidt, Julian Rupprecht, Jan Raba, David Jordan, Julian Frank, Daniel Staab, Steffen Dietmayer, Klaus Mercedes-Benz R&d North America SunnyvaleCA United States Mercedes-Benz Ag R&d Stuttgart Germany Ulm University Institute of Measurement Control and Microtechnology Ulm Germany University of Stuttgart Institute of Parallel and Distributed Systems Stuttgart Germany University of Southampton Electronics and Computer Science Southampton United Kingdom

Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Network encoder and task-specific decoders. The heterogeneous graphs, whose structures are defined by an ontology, consist of different nodes with type-specific node features and different relations with type-specific edge features. In order to exploit all the information given by these graphs, we propose to use cascaded layers of graph convolution. The result is an encoding of the scene. Task-specific decoders can be applied to predict desired attributes of the scene. Extensive evaluation on two diverse binary node classification tasks show the main strength of this methodology: despite being generic, it even manages to outperform task-specific baselines. The further application of our methodology to the task of node classification in various knowledge graphs shows its transferability to other domains. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning

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TS-MoCo: Time-Series Momentum Contrast for Self-Supervised P...

European Signal Processing Conference (EUSIPCO)

作者： Philipp Hallgarten David Bethge Ozan Özdcnizci Tobias Grosse-Puppendahl Enkelejda Kasneci Dr. Ing. h.c. F. Porsche AG Stuttgart Germany TU Munich Germany LMU Munich Germany Institute of Theoretical Computer Science TU Graz Austria TU Graz - SAL Dependable Embedded Systems Lab Silicon Austria Labs Graz Austria

Limited availability of labeled physiological data often prohibits the use of powerful supervised deep learning models in the biomedical machine intelligence domain. We approach this problem and propose a novel encoding framework that relies on self-supervised learning with momentum contrast to learn representations from multivariate time-series of various physiological domains without needing labels. Our model uses a transformer architecture that can be easily adapted to classification problems by optimizing a linear output classification layer. We experimentally evaluate our framework using two publicly available physiological datasets from different domains, i.e., human activity recognition from embedded inertial sensory and emotion recognition from electroencephalography. We show that our self-supervised learning approach can indeed learn discriminative features which can be exploited in downstream classification tasks. Our work enables the development of domain-agnostic intelligent systems that can effectively analyze multivariate time-series data from physiological domains.

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Sustainable electric vehicles fault detection based on monitoring by deep Learning Architectures in feature extraction and classification

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2023年 57卷

作者： Wongchai, Anupong Aoudni, Yassine Yesubabu, Mannava Reegu, Faheem Ahmad Gowri, N. Vasantha Vijayakumar, P. [a]The Independent researcher and Social Worker Thailand [b]Department of Computers and Information Technology Faculty of Sciences and Arts Turaif Northern Border University Arar 91431 Saudi Arabia [c]Computer & Embedded Systems Laboratory National School of Engineers of Sfax (ENIS) University of Sfax BP 1173 3038 Sfax Tunisia [d]Department Of Computer Science & Engineering Vardhaman College Of Engineering Shamshabad Hyderabad 501218 India [e]College of Computer Science And Information Technology Jazan University Jazan 45142 Saudi Arabia [f]Department of EEE Chaitanya Bharathi Institute of Technology (A) Hyderabad India [g]School of Electronics Engineering Vellore Institute of Technology (VIT) Chennai Tamil Nadu India

Numerous industrial sector paradigms have been altered by the necessity to produce more competitive ma-chinery and the introduction of digital technologies from so-called Industry 4.0. This research proposes novel technique in electric vehicle fault detection based on monitoring data classification and feature extraction using deep learning architectures. The real Proton exchange membrane fuel cell (PEMFC) experiment dataset has been collected as sustainable electric vehicles data using multi-cell parallel electric vehicle and it has been processed for noise removal, dimensionality reduction and extraction using deep stacking auto gradient descent for-classifyingthrough monte Carlo regressive Gaussian naive bayes *** of experiments demonstrate that suggested model achieves over 99% accuracy in identifying flooding fault of fuel cell under load-varying situations. The experimental analysis has been carried out in terms of accuracy, robustness, reliability, preci-sion, recall. The proposed technique attained 99% of accuracy, 89% of robustness, 85% of Reliability, 95% of precision and 81% of recall.

关键词： Electric vehicles Fault detection Monitoring Data classification Feature extraction Deep learning