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arXiv 2020年

作者： Löffler, Felicitas Wesp, Valentin König-Ries, Birgitta Klan, Friederike Heinz-Nixdorf Chair for Distributed Information Systems Department of Mathematics and Computer Science Friedrich Schiller University Jena Jena Germany Michael-Stifel-Center for Data-Driven and Simulation Science Jena Germany Halle-Jena-Leipzig Germany Citizen Science Group DLR-Institute of Data Science German Aerospace Center Jena Germany

The increasing amount of publicly available research data provides the opportunity to link and integrate data in order to create and prove novel hypotheses, to repeat experiments or to compare recent data to data collected at a different time or place. However, recent studies have shown that retrieving relevant data for data reuse is a time-consuming task in daily research practice. In this study, we explore what hampers dataset retrieval in biodiversity research, a field that produces a large amount of heterogeneous data. We analyze the primary source in dataset search - metadata - and determine if they reflect scholarly search interests. We examine if metadata standards provide elements corresponding to search interests, we inspect if selected data repositories use metadata standards representing scholarly interests, and we determine how many fields of the metadata standards used are filled. To determine search interests in biodiversity research, we gathered 169 questions that researchers aimed to answer with the help of retrieved data, identified biological entities and grouped them into 13 categories. The categories were evaluated with nine biodiversity scholars who assigned one of the types to pre-labeled biological entities in the questions. Our findings indicate that environments, materials and chemicals, species, biological and chemical processes, locations, data parameters and data types are important search interests in biodiversity research. The comparison with existing metadata standards shows that domain-specific standards cover search interests quite well, whereas general standards do not explicitly contain elements that reflect search interests. We inspect metadata from five large data repositories. Our results confirm that metadata currently poorly reflect search interests in biodiversity research. From these findings, we derive recommendations for researchers and data repositories how to bridge the gap between search interest and metadata provided.

关键词： Semantic Web

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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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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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Panel on future challenges in modeling methodology 04

Panel on future challenges in modeling methodology

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Simulation Winter Conference

作者： S.J.E. Taylor P. Lendermann R.J. Paul S.W. Reichenthal S. Strassburger S.J. Turner Centre for Applied Simulation Modeling Department of Information Systems and Computing Brunei University Uxbridge Middlesex UK Production and Logistics Planning Group Singapore Institute of Manufacturing Technology Singapore Boeing Anaheim CA USA Department Virtual Development Fraunhofer Institute of Factory Operation and Automation Magdeburg Germany Parallel & Distributed Computin Centre School of Computer Engineering Nanyang Technological University Singapore

ISBN: (纸本)9780780387867

This panel paper presents the views of six researchers and practitioners of simulation modeling. Collectively we attempt to address a range of key future challenges to modeling methodology. It is hoped that the views of this paper, and the presentations made by the panelists at the 2004 Winter Simulation Conference will raise awareness and stimulate further discussion on the future of modeling methodology in areas such as modeling problems in business applications, human factors and geographically dispersed networks; rapid model development and maintenance; legacy modeling approaches; markup languages; virtual interactive process design and simulation; standards; and Grid computing.

关键词： Computational modeling Virtual manufacturing Logistics Human factors computer aided manufacturing Markup languages Process design Standards development Grid computing Discrete event simulation

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Time-invariant degree growth in preferential attachment network models

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Physical Review E 2020年第2期101卷 022309-022309页

作者： Jun Sun (孙骏) Matúš Medo Steffen Staab Institute for Web Science and Technologies Universität Koblenz–Landau 56070 Koblenz Germany Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 610054 People's Republic of China Department of Radiation Oncology Inselspital Bern University Hospital and University of Bern 3010 Bern Switzerland and Department of Physics University of Fribourg 1700 Fribourg Switzerland Institute for Parallel and Distributed Systems Universität Stuttgart 70569 Stuttgart Germany and Web and Internet Science Research Group University of Southampton Southampton SO17 1BJ United Kingdom

Preferential attachment drives the evolution of many complex networks. Its analytical studies mostly consider the simplest case of a network that grows uniformly in time despite the accelerating growth of many real networks. Motivated by the observation that the average degree growth of nodes is time invariant in empirical network data, we study the degree dynamics in the relevant class of network models where preferential attachment is combined with heterogeneous node fitness and aging. We propose an analytical framework based on the time invariance of the studied systems and show that it is self-consistent only for two special network growth forms: the uniform and the exponential network growth. Conversely, the breaking of such time invariance explains the winner-takes-all effect in some model settings, revealing the connection between the Bose-Einstein condensation in the Bianconi-Barabási model and similar gelation in superlinear preferential attachment. Aging is necessary to reproduce realistic node degree growth curves and can prevent the winner-takes-all effect under weak conditions. Our results are verified by extensive numerical simulations.

关键词： Degree distributions Network formation & growth Preferential attachment Real world networks Barabasi-Albert model Fitness models Preferential attachment models

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Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

arXiv

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arXiv 2022年

作者： Kokkonen, Henna Lovén, Lauri Motlagh, Naser Hossein Kumar, Abhishek Partala, Juha Nguyen, Tri Pujol, Víctor Casamayor Kostakos, Panos Leppänen, Teemu González-Gil, Alfonso Sola, Ester Angulo, Iñigo Liyanage, Madhusanka Bennis, Mehdi Tarkoma, Sasu Dustdar, Schahram Pirttikangas, Susanna Riekki, Jukka Center for Ubiquitous Computing University of Oulu Finland Center for Machine Vision and Signal Analysis University of Oulu Finland Center for Wireless Communications University of Oulu Finland School of Computer Science University College Dublin Ireland Department of Computer Science University of Helsinki Finland Distributed Systems Group TU Wien Austria R&D Department *** S.L. Spain

Future AI applications require performance, reliability, and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on edge AI for resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration. © 2022, CC BY-NC-ND.

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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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HUNTER: AI based holistic resource management for sustainable cloud computing

arXiv

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arXiv 2021年

作者： Tuli, Shreshth Gill, Sukhpal Singh Xu, Minxian Garraghan, Peter Bahsoon, Rami Prof, Scharam Dustdar Sakellariou, Rizos Rana, Omer Buyya, Rajkumar Casale, Giuliano Jennings, Nicholas R. Department of Computing Imperial College London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China School of Computing and Communications Lancaster University United Kingdom School of Computer Science University of Birmingham United Kingdom Distributed Systems Group Vienna University of Technology Austria Department of Computer Science University of Manchester United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively. © 2021, CC BY.

关键词： Energy efficiency

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Towards Holographic Video Communications: A Promising AI-driven Solution

arXiv

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arXiv 2022年

作者： Huang, Yakun Zhu, Yuanwei Qiao, Xiuquan Su, Xiang Dustdar, Schahram Zhang, Ping The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Computer Science Norwegian University of Science and Technology Gjøvik2815 Norway University of Oulu Oulu90570 Finland The Distributed Systems Group Technische Universität Wien Vienna1040 Austria

Real-time holographic video communications enable immersive experiences for next-generation video services in the future metaverse era. However, high-fidelity holographic videos require high bandwidth and significant computation resources, which exceed the transferring and computing capacity of 5G networks. This article reviews state-of-the-art holographic point cloud video (PCV) transmission techniques and highlights the critical challenges of delivering such immersive services. We further implement a preliminary prototype of an AI-driven holographic video communication system and present critical experimental results to evaluate its performance. Finally, we identify future research directions and discuss potential solutions for providing real-time and high-quality holographic experiences. Copyright © 2022, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Multi-Player Immersive Communications and Interactions in Metaverse: Challenges, Architecture, and Future Directions

arXiv

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arXiv 2022年

作者： Huang, Yakun Qiao, Xiuquan Wang, Haowen Su, Xiang Dustdar, Schahram Zhang, Ping State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science Norwegian University of Science and Technology 2815 Gjøvik Norway University of Oulu Oulu90570 Finland Distributed Systems Group Technische Universität Wien Vienna1040 Austria

The metaverse has awakened users’ expectations of an immersive interaction that fuses the virtual digital world and the physical world across space and time. However, the metaverse is still in its infancy, typically expanding multi-player applications (e.g., multi-player games) to implement a prototype with the help of 5G/Beyond 5G, Artificial Intelligence, digital twin, and other enabling technologies. This article reviews the characteristics, key enabling technologies, and driving applications of the state-of-the-art metaverse. We focus on the immersive interactions perspective of the metaverse from the tasks, inputs, and feedback across the users, digital world, and physical world and reveal the key challenges. Afterwards, we present a multi-player interaction prototype platform based on a cloud-edge-device collaborative framework. Also, we evaluate it with centralized and device-to-device (D2D) approaches to verify the efficiency and flexibility of interactions. Finally, we point out future research approaches and discuss potential solutions to enable more stable and higher quality multi-player interactions for metaverse services. Copyright © 2022, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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