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28th IEEE Symposium on computers and communications, ISCC 2023

作者： Borsatti, Davide Cerroni, Walter Foschini, Luca Grabarnik, Genady Ya. Poltronieri, Filippo Scotece, Domenico Shwartz, Larisa Stefanelli, Cesare Tortonesi, Mauro Zaccarini, Mattia University of Bologna Bologna Italy St. John's University Department of Mathematics and Computer Science QueensNY United States University of Ferrara Distributed Systems Research Group Ferrara Italy

ISBN: (纸本)9798350300482

Kubernetes provides several functions that can help service providers to deal with the management of complex container-based applications. However, most of these functions need a time-consuming and costly customization process to address service-specific requirements. The adoption of Digital Twin (DT) solutions can ease the configuration process by enabling the evaluation of multiple configurations and custom policies by means of simulation-based what-if scenario analysis. To facilitate this process, this paper proposes KubeTwin, a framework to enable the definition and evaluation of DTs of Kubernetes applications. Specifically, this work presents an innovative simulation-based inference approach to define accurate DT models for a Kubernetes environment. We experimentally validate the proposed solution by implementing a DT model of an image recognition application that we tested under different conditions to verify the accuracy of the DT model. The soundness of these results demonstrates the validity of the KubeTwin approach and calls for further investigation. © 2023 IEEE.

关键词： Function evaluation

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Assessing mowing intensity:A new index incorporating frequency,type of machinery,and technique

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Grassland research 2024年第3期3卷 264-274页

作者： Margarita Hartlieb Sebastian Raubitzek Johanna L.Berger Michael Staab Juliane Vogt Manfred Ayasse Andreas Ostrowski Wolfgang Weisser Nico Blüthgen Ecological Networks Department of BiologyTechnical University of DarmstadtDarmstadtGermany SBA-Research ViennaAustria Natura 2000-Station Unstrut-Hainich/Eichsfeld Wildtierland Hainich gGmbHHörselberg-HainichGermany Institute of Evolutionary Ecology and Conservation Genomics Ulm UniversityUlmGermany Heinz Nixdorf Chair for Distributed Information Systems Faculty of Mathematics and Computer ScienceFriedrich Schiller UniversityJenaGermany Department of Life Science Systems School of Life SciencesTechnical University of MunichTerrestrial Ecology Research GroupFreisingGermany

Background:Only a few decades ago,colorful,small-scale,heterogeneous,and species-rich hay meadows or extensive pastures were common,but have often been replaced by species-poor,uniform,large-scale multicut *** advancements and improved efficiency in grassland management have come at the cost of ***:In Germany,150 grassland plots have been investigated since *** these extensive data,we propose a new compound index for estimating the site-specific mowing intensity in order to facilitate assessment of the impact of mowing intensity on biodiversity and ecosystem *** index integrates the various qualitative components of mowing machine type,mowing height and use of a conditioner,with the annual number of ***:The newly proposed index achieves a much finer gradation of mowing intensity compared to the previous quantification based on the number of cuts ***,a decrease in plant and arthropod species was observed at higher mowing ***:The proposed mowing intensity index offers enhanced precision in calculations and can easily be integrated into assessments of land-use intensity in ***,it could serve as a basis for providing subsidies to farmers,who adopt low-impact mowing practices.

关键词： applications conditioner mower mowing height number of cuts species richness

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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TELKA: Twin-Enhanced Learning for Kubernetes Applications

TELKA: Twin-Enhanced Learning for Kubernetes Applications

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IEEE Symposium on computers and communications (ISCC)

作者： Mattia Zaccarini Davide Borsatti Walter Cerroni Luca Foschini Genady Ya. Grabarnik Lorenzo Manca Filippo Poltronieri Domenico Scotece Larisa Shwartz Cesare Stefanelli Mauro Tortonesi Distributed Systems Research Group University of Ferrara Ferrara Italy University of Bologna Bologna Italy Department of Mathematics and Computer Science St. John’s University Queens NY USA Operational Innovation IBM TJ Watson Research Center NY USA

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

Chaos engineering is the discipline of injecting computing and network faults, such as increased network latency and unavailability of computing nodes, into an IT system to help developers in identifying problems that could arise in a production environment and tackle them. Several tools have emerged to ease the application of chaos engineering to complex IT systems, leveraging microservice and container-based applications deployed on Kubernetes. However, applying of such tools requires several phases to be put into practice, from defining a steady state to establishing an effective response plan if something goes wrong. To ease the application of chaos engineering in improving the resilience of Kubernetes applications, this work presents a smart scheduler for Kubernetes called TELKA: a Twin-Enhanced Learning for Kubernetes Applications, which combines chaos engineering, Digital Twin (DT), and Reinforcement Learning (RL) methodologies to mitigate the effects of computing and network faults. Instead of interacting directly with the physical Kubernetes application, TELKA learns by interacting with a digital twin, thus reducing the learning time and the operation costs related to the application of chaos engineering. Experiment results compare TELKA with other approaches to show its effectiveness in mitigating the adverse effects of injected faults.

关键词： Chaos Fault diagnosis computers Costs Microservice architectures Reinforcement learning Production Digital twins Steady-state Resilience

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distributed AI in Zero-touch Provisioning for Edge Networks: Challenges and research Directions

arXiv

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arXiv 2023年

作者： Hazra, Abhishek Morichetta, Andrea Murturi, Ilir Lovén, Lauri Dehury, Chinmaya Kumar Pujol, Víctor Casamayor Donta, Praveen Kumar Dustdar, Schahram Department of Computer Science and Engineering the Indian Institute of Information Technology Sri City India Communications & Networks Lab Department of Electrical and Computer Engineering National University of Singapore Singapore119260 Singapore Distributed Systems Group TU Wien Vienna1040 Austria Center for Ubiquitous Computing University of Oulu 90014 Finland Institute of Computer Science University of Tartu Tartu51009 Estonia

Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively offer computation and storage resources. This transformation presents substantial challenges to network administration and service providers regarding sustainability and scalability. This article combines distributed Artificial Intelligence (DAI) with Zero-touch Provisioning (ZTP) for edge networks. This combination helps to manage network devices seamlessly and intelligently by minimizing human intervention. In addition, several advantages are also highlighted that come with incorporating distributed AI into ZTP in the context of edge networks. Further, we draw potential research directions to foster novel studies in this field and overcome the current limitations. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Characterization of Microservice Response Time in Kubernetes: A Mixture Density Network Approach

Characterization of Microservice Response Time in Kubernetes...

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International Conference on Network and Service Management

作者： Lorenzo Manca Davide Borsatti Filippo Poltronieri Mattia Zaccarini Domenico Scotece Gianluca Davoli Luca Foschini Genady Ya. Grabarnik Larisa Shwartz Cesare Stefanelli Mauro Tortonesi Walter Cerroni University of Bologna Bologna Italy Distributed Systems Research Group University of Ferrara Ferrara Italy Department of Mathematics and Computer Science St. John's University Queens NY USA Operational Innovation IBM TJ Watson Research Center NY USA

The use of microservice-based applications is becoming more prominent also in the telecommunication field. The current 5G core network, for instance, is already built around the concept of a “Service Based Architecture”, and it is foreseeable that 6G will push even further this concept to enable more flexible and pervasive deployments. However, the increasing complexity of future networks calls for sophisticated platforms that could help network providers with their deployments design. In this framework, a central research trend is the development of digital twins of the physical infrastructures. These digital representations should closely mimic the behavior of the managed system, allowing the operators to test new configurations, analyze what-if scenarios, or train their reinforcement learning algorithms in safe environments. Considering that Kubernetes is becoming the de-facto standard platform for container orchestration and microservice-based application lifecycle management, the implementation of a Kubernetes digital twin requires an accurate characterization of the microservice response time, possibly leveraging suitable Machine Learning techniques trained with measurement data collected in the field. In this paper we introduce a new methodology, based on Mixture Density Networks, to accurately estimate the statistical distribution of the response time of microservice-based applications. We show the improvement in performance with respect to simulation-based inference procedures proposed in literature.

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on computer Aided systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Using Simulation and DT to Improve Performance of systems Operation Modeling 23

Using Simulation and DT to Improve Performance of Systems Op...

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Proceedings of the 33rd Annual International Conference on computer science and Software Engineering

作者： Grabarnik Genady Ya. Poltronieri Filippo Tortonesi Mauro Zaccarini Mattia Shwartz Larisa Department of Mathematics and Computer Science St. John’s University Queens New York USA Distributed Systems Research Group University of Ferrara Ferrara Italy AI for IT IBM T.J. Watson Research Center Yorktown Heights New York USA

The ever-increasing complexity of applications and networks man-agement demands alternative solutions for identifying of suitable deployment configurations in line with established Service Level Agreements. Some existing approaches utilize methods such as mathematical simulation and application re-enactment under a sim-ulated load implemented as digital twin. In this paper, we describe a mixed-modal framework for varying composition of the above approaches hence improving the performance of application’s ser-vice level modeling further. This framework facilitates the best simulation model performance at any given time by anticipating the model drift and by continuously monitoring performance of the mixed-modal simulation model.

关键词： System Operation Modeling Simulation Mathematical Simulation Re-Enactment Simulation Digital Twin

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Modeling Digital Twins of Kubernetes-Based Applications

Modeling Digital Twins of Kubernetes-Based Applications

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IEEE Symposium on computers and communications (ISCC)

作者： Davide Borsatti Walter Cerroni Luca Foschini Genady Ya. Grabarnik Filippo Poltronieri Domenico Scotece Larisa Shwartz Cesare Stefanelli Mauro Tortonesi Mattia Zaccarini University of Bologna Bologna Italy Department of Mathematics and Computer Science St. John's University Queens NY USA Distributed Systems Research Group University of Ferrara Ferrara Italy IBM TJ W2atson Research Center Operational Innovation NY USA

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An Efficient Multi-band Infrared Small Objects Detection Approach for Low-Altitude Artificial Intelligence of Thing

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IEEE Internet of Things Journal 2025年

作者： Peng, Yuhuai Wang, Jing Wang, Wenqian Liu, Lei Atiquzzaman, Mohammed Guizani, Mohsen Dustdar, Schahram Northeastern University School of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Hangzhou310024 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China University of Oklahoma School of Computer Science NormanOK73019 United States Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Masdar United Arab Emirates Technische Universität Wien Distributed Systems Group Vienna1040 Austria

As a cutting-edge technology of low-altitude Artificial Intelligence of Thing (AIoT), UAV object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multi-band infrared small object detection approach for low-altitude intelligent surveillance services. Firstly, we propose the Multi-band infrared Image Fusion algorithm based on Cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based Multi-scale Dense Small Object Detection (MsDSOD) algorithm is proposed. The algorithm consists of the Global-Local Object Detection (G-LOD) network, the Object Dense Area Extraction (O-DAE) module, and the Weighted Boxes Fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean Average Precision (mAP) of MsDSOD is improved by 0.80% and the Average Precision in small object detection (APs) is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion. © 2014 IEEE.

关键词： Infrared imaging

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