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Extending OpenStack Monasca for Predictive Elasticity Control

Big Data Mining and Analytics 2024年第2期7卷 315-339页

作者： Giacomo Lanciano Filippo Galli Tommaso Cucinotta Davide Bacciu Andrea Passarella Scuola Normale Superiore Pisa 56126Italy the Real-Time Systems Laboratory(RETIS) TelecommunicationsComputer Engineeringand Photonics Institute(TeCIP)Scuola Superiore Sant’AnnaPisa 56127Italy Department of Computer Science University of PisaPisa 56127Italy National Research Council Pisa56127Italy

Traditional auto-scaling approaches are conceived as reactive automations,typically triggered when predefined thresholds are breached by resource consumption *** such rules at scale is cumbersome,especially when resources require non-negligible time to be *** paper introduces an architecture for predictive cloud operations,which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the *** this way,they can anticipate load peaks and trigger appropriate scaling actions in advance,such that new resources are available when *** proposed architecture is implemented in OpenStack,extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard *** use our architecture to implement predictive scaling policies leveraging on linear regression,autoregressive integrated moving average,feed-forward,and recurrent neural networks(RNN).Then,we evaluate their performance on a synthetic workload,comparing them to those of a traditional *** assess the ability of the different models to generalize to unseen patterns,we also evaluate them on traces from a real content delivery network(CDN)*** particular,the RNN model exhibites the best overall performance in terms of prediction error,observed client-side response latency,and forecasting *** implementation of our architecture is open-source.

关键词： OpenStack monitoring elasticity control auto-scaling predictive operations Monasca

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Scalable and Low-Cost Remote Lab Platforms: Teaching Industrial robotics Using Open-Source Tools and Understanding Its Social Implications 16th

Scalable and Low-Cost Remote Lab Platforms: Teaching Indust...

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Kumar, Amit Jose, Jaison Jain, Archit Kulkarni, Siddharth Arya, Kavi Centre for Systems and Control Indian Institute of Technology Bombay Mumbai India Embedded Real-Time Systems / e-Yantra Lab Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Department Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9789819635214

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Costs

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A Mathematical Comparison Between Response-time Analysis and real-time Calculus for Fixed-Priority Preemptive Scheduling 34

A Mathematical Comparison Between Response-Time Analysis and...

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34th Euromicro Conference on real-time systems, ECRTS 2022

作者： Pollex, Victor Slomka, Frank INCHRON AG Erlangen Germany Institute of Embedded Systems/Real-Time Systems Faculty of Engineering and Computer Science Universität Ulm Germany

ISBN: (纸本)9783959772396

Fixed-priority preemptive scheduling is a popular scheduling scheme for real-time systems. This is accompanied by a vast amount of research on how to analyse and check whether these systems satisfy their real-time requirements. Two methods that emerged from this research are the response-time analysis and the real-time calculus. These two methods have been compared empirically on the basis of several abstract systems showing that for some systems one method gives better results than the other and for other systems both methods appear to give the same results. However, empirical analyses inherently contain uncertainty. To get a definitive answer we compare both methods mathematically and we show that both methods give the same results for systems that use fixed-priority preemptive scheduling and independent tasks. © Victor Pollex and Frank Slomka

关键词： real time systems

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Optimizing Very Large Scale ITS Applications with Fast Fitness Evaluation

Optimizing Very Large Scale ITS Applications with Fast Fitne...

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IEEE Conference on Wireless Communications and Networking

作者： Nagacharan Teja Tangirala Rouven Rischert Christoph Sommer Alois Knoll Chair of Robotics Artificial Intelligence and Real-time Systems Technical University of Munich Munich Germany Faculty of Computer Science TU Dresden Germany

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

As wireless communication technology advances, the complexity of Vehicular Ad-hoc Network (VANET) simulations increases. This, however, is at odds with the need for increasingly large-scale Intelligent Transportation systems (ITS) scenarios to satisfy the demands of increasingly Artificial Intelligence (AI) based solutions. This paper aims to demonstrate that a high-performance simplified VANET simulator can be used for fitness evaluation without loss in solution quality. As an example, we implement a Roadside Unit (RSU) deployment approach based on a genetic algorithm. Disolv, a simplified VANET simulator, is used as a fitness evaluation tool. To validate the solution quality, the best solutions of a few select generations are evaluated with a fully-featured ns-3 driven VANET simulation. From the comparison of fitness values, it can be observed that the values from Disolv allow us to predict those obtained via ns-3. Further, the execution time analysis showcases the substantial performance gains of using a more abstract VANET simulator. A 4.6-hour analysis with Disolv contrasts approximately 300 days with ns-3 for the given scenario and settings. Finally, the potential applications and limitations of using a simplified VANET simulator are discussed.

关键词： Wireless communication Vehicular ad hoc networks Performance gain Complexity theory Artificial intelligence Optimization Intelligent transportation systems Genetic algorithms

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Node Classification in CAD Data: A Graph Learning Approach for Machining Feature Localization 4

Node Classification in CAD Data: A Graph Learning Approach f...

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4th International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Bohm, Stefan Andreas Neumayer, Martin Song, Rui Rib, Fabian Knoll, Alois Technical University of Munich Chair of Robotics Artificial Intelligence and Real-Time Systems Munich Germany Rosenheim Technical University of Applied Sciences Research and Development Department Rosenheim Germany Fraunhofer Institute for Transportation and Infrastructure Systems Ingolstadt Germany Rosenheim Technical University of Applied Sciences Faculty of Engineering Sciences Rosenheim Germany

ISBN: (纸本)9798350391183

Pattern recognition is becoming increasingly important in many sectors, such as the manufacturing industry. With rising customer demands and advancing technology, product data, often represented as 3D meshes, is becoming highly complex. Therefore, intelligent approaches are needed to recognize and localize features in 3D meshes to determine complex manufacturing processes. However, previous approaches solely work on voxel-, pixel-or. STEP-based formats and not directly on mesh-based formats such as STL data. To overcome these issues, we present a configurable data generator and customize graph neural network architectures for the problem of localizing machining features directly in 3D meshes. After comparing the graph neural networks against each other, the best resulting network is compared to an existing state-of-The-Art approach, i.e., SsdNet. Our results indicate that the best-performing graph neural network exhibits superior classification accuracies on meshes with few machining features compared to SsdNet with an average F1 score of 80%. While the accuracy slightly falls behind SsdNet with more features, the graph neural network shows substantially better runtime performance results, which are, on average, 70 times faster. © 2024 IEEE.

关键词： Graph neural networks

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Poster: Disolv - Dataflow-Centric Integrated Simulation of Large-Scale VANETs

Poster: Disolv - Dataflow-Centric Integrated Simulation of L...

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IEEE Vehicular Networking Conference (VNC)

作者： Nagacharan Teja Tangirala Christoph Sommer Alois Knoll Chair of Robotics Artificial Intelligence and Real-time Systems Technical University of Munich Munich Germany Faculty of Computer Science TU Dresden Germany

ISBN: (数字)9798350362701

ISBN: (纸本)9798350362718

Large-scale VANET simulations are computationally intensive. Disolv is a simulation architecture proposed to support city-scale VANET studies. This paper describes software decisions taken to realize a concrete implementation of Disolv. We describe a common workflow and provide guidelines on best utilizing Disolv. Finally, a small experiment demonstrates the performance gains by comparing the execution times with state-of-the-art VANET simulators.

关键词： Computational modeling Vehicular ad hoc networks computer architecture Performance gain Software Guidelines

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Learning Dynamic Robot-to-Robot Object Handover

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IFAC-PapersOnLine 2023年第2期56卷 4368-4374页

作者： Yansong Wu Lingyun Chen Ignacio Perez Mahiques Zhenshan Bing Fan Wu Alois Knoll Sami Haddadin Centre for Tactile Internet with Human-in-the-Loop (CeTI) The Chair of Robotics and Systems Intelligence MIRMI - Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany The Chair of Robotics Artificial Intelligence and Real-time Systems Technical University of Munich Germany

Object handover is an essential skill for collaborative robots in both services robotics and manufacturing scenarios. Most previous works were conducted from the perspective of human-robot interaction. The object handover between robots for collaborative task execution, aiming at optimizing time efficiency with human-like smooth behaviour, has not been extensively addressed. In this work, we propose a skill framework based on variable impedance control and dynamic motion primitives to optimize not only the motion trajectories and variable impedance, but also the timing of hand actions during dynamic motion. The effectiveness of the proposed framework is evaluated on a real dual-arm robot system under two handover scenarios with different constraints on the timing of hand actions. The experiment results demonstrate significant time efficiency improvement with reduction of the execution time by 9.4% and 23.7%, compared with accelerating the motion speed of the demonstrated handover. Furthermore, it can be observed that the robot successfully learned dynamic object handover without requiring transfer action to be triggered after both hands stop and remain still. In addition, in the second experiment, it is shown that the object can be transferred even without ensuring firm contact, which indicates that object handover is possible to be realized by throwing-like motion.

关键词： Intelligent robotics autonomous robotic systems robots manipulators dynamic movement primitive object handover

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Online Reduction of Exploration Space for Automated Underwater Modem Optimization

Online Reduction of Exploration Space for Automated Underwat...

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MILCOM, Military Communications Conference

作者： Marcel Rieß Steffen Moser Frank Slomka Institute of Embedded Systems/Real-Time Systems Faculty of Engineering Computer Science and Psychology Ulm University Germany

Developing efficient underwater communication systems is complex, as multiple factors impact communication quality. While rapid prototyping has been a common approach, it carries a high risk of failure. To address this challenge, model-based development and implementation have gained popularity. However, selecting the optimal combination of modulation type, correction properties, synchronization properties, and other parameters remains time-consuming and intricate. This paper presents an innovative approach to streamline the development process by reducing the exploration space. The proposed method evaluates potential solutions based on scenario-specific, application-specific, and performance-related upper and lower bounds for the selectable communication system. These bounds consider common underwater challenges, such as SNR degradation and multiple bounce loss, and distinguish between dynamic and static system analyses. By adopting this approach, existing analysis and development workflow achieve more optimized communication systems and afford more efficient variant selection using narrowing down the solution space and considering the specific requirements of each scenario. The approach’s effectiveness is demonstrated through case studies and evaluations, highlighting the benefits of reducing the exploration space and utilizing scenario-, application-, and performance-based bounds. This enables the development of robust and tailored underwater communication systems, improving communication quality and overall system performance.

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Named Entity Recognition of Electronic Medical Records based on BERT-BiLSTM-Biaffine Model

Named Entity Recognition of Electronic Medical Records based...

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Wang, Peng Gu, Jinguang Institute of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Prov. Key Laboratory of Intelligent Information Processing and Real Time Industrial Systems Wuhan University of Science and Technology Wuhan China

In the case of the specific task of identifying named entities within electronic medical record, it is hard to determine the boundary of nested entities, and existing NER systems have insufficient decoding performance. Based on the pre training model BERT, this paper introduces a novel network structure called Biaffine Layer using a bidirectional LSTM layer. The network uses a dual affine attention mechanism for semantic information learning, which can better interact with the semantic information of entity heads and entity tails, thereby achieving better recognition results for entities. Due to the sparsity of named entity datasets and the uneven distribution of entity categories, traditional binary cross entropy loss functions require multiple rounds of training to decode entities. In this paper, we have modified the binary cross entropy loss function to make the proposed model faster decode the entities that need to be identified. The model performs well, according to the experimental findings. The approach suggested in this paper offers a fresh approach to the NER issue raised by electronic medical records, and it is anticipated to considerably boost the effectiveness and caliber of clinical medical research. © Published under licence by IOP Publishing Ltd.

关键词： Decoding

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DD-Net: Dynamic Network Architecture for Optimized Curve Segmentation and Reduce Computational Redundancy 27th

DD-Net: Dynamic Network Architecture for Optimized Curve Seg...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cao, Yunxiang Chen, Li Wang, Yubo Feng, Zhida Tian, Jing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Institute of Systems Science National University of Singapore Singapore Singapore

ISBN: (纸本)9783031783821

Curvilinear Structure segmentation has numerous applications in various fields, including providing a better understanding of defects such as cracks on roads or walls, thereby assessing the structural safety of buildings. Numerous curve segmentation methods based on Convolutional Neural Networks(CNN) have been developed in recent years. However, preserving feature integrity remains a challenge. To address this issue, this paper introduces a double dynamic network, called DD-Net, which consists of a dynamic structure for training and dynamic masking for inference. Firstly, to enhance the nonlinearity of the model, a paradigm to auto-adjust the CNN structure via a trainable module is formed for better fine-grained feature extraction. This dynamic structure (DS) paradigm enables a trade-off to extract or discard the feature data in the model training. Due to the data heterogeneity between the training and inference data, models may contain some useless nodes that are less effective during inference. The dynamic masking (DM) is proposed to omit the useless nodes based on the score difference between the train and inference feature statistics, thereby reducing redundant computations. To further improve the model’s performance on thin-curve segmentation and to preserve feature integrity, we introduce a non-curve suppression (NCS) module. This module focuses on background information while considering foreground prediction to address noisy conditions. The experimental results show that our DD-Net achieves promising results on three benchmark datasets and outperforms state-of-the-art curve segmentation models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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