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作者： Calyam, Prasad Wilkins-Diehr, Nancy Miller, Mark Brookes, Emre H. Arora, Ritu Chourasia, Amit Jennewein, Douglas M. Nandigam, Viswanath Drew LaMar, M. Cleveland, Sean B. Newman, Greg Wang, Shaowen Zaslavsky, Ilya Cianfrocco, Michael A. Ellett, Kevin Tarboton, David Jeffery, Keith G. Zhao, Zhiming González-Aranda, Juan Perri, Mark J. Tucker, Greg Candela, Leonardo Kiss, Tamas Gesing, Sandra Department of Electrical Engineering and Computer Science University of Missouri ColumbiaMO United States San Diego Supercomputer Center University of California San DiegoCA United States Department of Biochemistry University of Texas Health Science Center HoustonTX United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Information Technology Services University of South Dakota VermillionSD United States Biology Department College of William and Mary WilliamsburgVA United States Information Technology Services–Cyberinfrastructure University of Hawaii HonoluluHI United States Natural Resource Ecology Laboratory Colorado State University Fort CollinsCO United States Department of Geography and Geographic Information Science University of Illinois UrbanaIL United States Department of Biological Chemistry and Life Sciences Institute University of Michigan Ann ArborMI United States Pervasive Technology Institute Indiana University BloomingtonIN United States Civil and Environmental Engineering Utah State University LoganUT United States Keith G. Jeffery Consultants Faringdon United Kingdom Multiscale Networked Systems Group University of Amsterdam Amsterdam Netherlands Chief Technology Office LifeWatch ERIC Seville Spain Chemistry Department Sonoma State University Rohnert ParkCA United States Department of Geological Sciences CIRES University of Colorado Boulder BoulderCO United States National Research Council of Italy Pisa Italy Research Center for Parallel Computing University of Westminster London United Kingdom Center for Research Computing University of Notre Dame Notre DameIN United States

Scholars worldwide leverage science gateways/virtual research environments (VREs) for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this article, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of, for example, their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standards in cloud computing platforms, web service architectures, data management tools and cybersecurity. Correspondingly, we present a number of technology advances that could be incorporated in next-generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest group (VRE-IG) of the Research Data Alliance. Thus, community-driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations. © 2020 John Wiley & Sons, Ltd.

关键词： Web services

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An energy-aware resource provisioning scheme for real-time applications in a cloud data center

An energy-aware resource provisioning scheme for real-time a...

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作者： Faragardi, Hamid Reza Dehnavi, Saeid Nolte, Thomas Kargahi, Mehdi Fahringer, Thomas School of Innovation Design and Engineering Mälardalen University Västerås Sweden Distributed and Parallel Systems Group Institute of Computer Science Innsbruck University Innsbruck Austria School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Tehran Iran

Based on a pay-as-you-go model, cloud computing provides the possibility of hosting pervasive applications from both academic and business domains. However, data centers hosting cloud applications consume huge amounts of electrical energy, contributing to high operational costs and large carbon footprints to the environment. Energy-aware resource provisioning is an effective solution to diminish the energy consumption of cloud data centers. Recently, a growing trend has emerged, where cloud technology is used to run periodic real-time applications such as multimedia, telecommunication, video gaming, and industrial applications. In order for a real-time application to be able to use cloud services, cloud providers have to be able to provide timing guarantees. In this paper, we introduce an energy-aware resource provisioning mechanism for cloud data centers, which are capable of serving real-time periodic tasks following the Software as a Service model. The proposed method is compared against an energy-aware version of the RT-OpenStack. RT-OpenStack is a recently proposed approach to provide a time-predictable version of OpenStack. The experimental results manifest that our proposed resource provisioning method outperforms energy-aware version of the RT-OpenStack by 16.01%, 25.45%, and 25.45% in terms of energy consumption, number of used servers, and average utilization of used servers, respectively. Moreover, from the scalability perspective, the preference of the proposed method for large-scale data centers is more considerable. Copyright © 2018 John Wiley & Sons, Ltd.

关键词： Ant colony optimization

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HomeCA: Scalable Secure IoT Network Integration 49

HomeCA: Scalable Secure IoT Network Integration

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49. Jahrestagung der Gesellschaft fur Informatik: 50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft, INFORMATIK 2019 - 49th Annual Meeting of the German Informatics Society: 50 years of the German Informatics Society - computer science for Society, INFORMATICS 2019

作者： Müller, Robert Schmitt, Corinna Kaiser, Daniel Waldvogel, Marcel Department of Computer and Information Science Distributed Systems Laboratory Universität Konstanz Konstanz78457 Germany Research Institute Code Universität der Bundeswehr München Werner-Heisenberg-Weg 39 Neubiberg85577 Germany Security and Networking Research Group Netlab Maison du Nombre 6 University of Luxembourg Avenue de la Fonte Esch-sur-Alzette4364 Luxembourg

ISBN: (纸本)9783885796886

Integrating Internet of Things (IoT) devices into an existing network is a nightmare. Minimalistic, unfriendly user interfaces, if any;badly chosen security methods, most notably the defaults;lack of long term security;and bugs or misconAgurations are plentiful. As a result, an increasing number of owners operate unsecure devices. Our investigations into the root causes of the problems resulted in the development of Home CertiAcate Authority (HomeCA). HomeCAincludes a comprehensive set of secure, vendor-independent interoperable practices based on existing protocols and open standards. HomeCA avoids most of the current pitfalls in network integration by design. Long-term protocol security, permission management, and secure usage combined with simpliAed device integration and secure key updates on ownership acquisition pave the way toward scalable, federated IoT security. © 2019 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Network security

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ABAC Based Online Collaborations in the Cloud 1st

ABAC Based Online Collaborations in the Cloud

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1st International Conference on Emerging Technologies for Developing Countries, AFRICATEK 2017

作者： Madani, Mohamed Amine Erradi, Mohammed Benkaouz, Yahya Networking and Distributed Systems Research Group SIME Lab ENSIAS Mohammed V University in Rabat Rabat Morocco LCS Department of Computer Science FSR Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9783319678368

Nowadays sharing data among organizations plays an important role for their collaboration. During collaborations, the organizations need to access shared information while respecting the access control constraints. In addition, most organizations rely on cloud based solutions to store their data (e.g. openstack). In such platform, data access is regulated by Access Control Lists (ACLs). ACL defines static access rules. It assumes the knowledge of the whole set of users and possible access requests. This make ACL unusable in collaborative context due to the dynamic nature of collaborative sessions. In this paper, we consider ABAC, a flexible and fine-grained model, as an access control model for cloud-based collaborations to overcome the ACL limitations. We provide an architecture that integrate ABAC in the storage level of a cloud platform. © 2018, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Digital storage

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distributed environment for efficient virtual machine image management in federated Cloud architectures

Distributed environment for efficient virtual machine image ...

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作者： Kimovski, Dragi Marosi, Attila Gec, Sandi Saurabh, Nishant Kertesz, Attila Kecskemeti, Gabor Stankovski, Vlado Prodan, Radu Distributed and Parallel Systems Group University of Innsbruck Innsbruck Austria Hungarian Academy of Science Budapest Hungary Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana Slovenia Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Liverpool John Moores University Liverpool United Kingdom University of Information Science and Technology Ohrid Macedonia

The use of virtual machines (VMs) in Cloud computing provides various benefits in the overall software engineering lifecycle. These include efficient elasticity mechanisms resulting in higher resource utilization and lower operational costs. The VMs as software artifacts are created using provider-specific templates, called virtual machine images (VMI), and are stored in proprietary or public repositories for further use. However, some technology-specific choices can limit the interoperability among various Cloud providers and bundle the VMIs with nonessential or redundant software packages, leading to increased storage size, prolonged VMI delivery, stagnant VMI instantiation, and ultimately vendor lock-in. To address these challenges, we present a set of novel functionalities and design approaches for efficient operation of distributed VMI repositories, specifically tailored for enabling (1) simplified creation of lightweight and size optimized VMIs tuned for specific application requirements;(2) multi-objective VMI repository optimization;and (3) efficient reasoning mechanism to help optimizing complex VMI operations. The evaluation results confirm that the presented approaches can enable VMI size reduction by up to 55%, while trimming the image creation time by 66%. Furthermore, the repository optimization algorithms can reduce the VMI delivery time by up to 51% and cut down the storage expenses by 3%. Moreover, by implementing replication strategies, the optimization algorithms can increase the system reliability by 74%. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Virtual machine

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Self-Awareness for Autonomous systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Cloud workload prediction based on workflow execution time discrepancies

arXiv

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arXiv 2018年

作者： Kecskemeti, Gabor Nemeth, Zsolt Kertesz, Attila Ranjan, Rajiv Department of Computer Science Liverpool John Moores University Laboratory of Parallel and Distributed Systems MTA SZTAKI Software Engineering Department University of Szeged School of Computing Newcastle University

Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase the reliability or performance of their applications, they would need solutions to detect behavioural changes in the underlying system. Existing runtime solutions for such purposes offer limited capabilities as they are mostly restricted to revealing weekly or yearly behavioural periodicity in the infrastructure. This article proposes a technique for predicting generic background workload by means of simulations that are capable of providing additional knowledge of the underlying private cloud systems in order to support activities like cloud orchestration or workflow enactment. Our technique uses long-running scientific workflows and their behaviour discrepancies and tries to replicate these in a simulated cloud with known (trace-based) workloads. We argue that the better we can mimic the current discrepancies the better we can tell expected workloads in the near future on the real life cloud. We evaluated the proposed prediction approach with a biochemical application on both real and simulated cloud infrastructures. The proposed algorithm has shown to produce significantly (∼20%) better workload predictions for the future of simulated clouds than random workload selection. Copyright © 2018, The Authors. All rights reserved.

关键词： Infrastructure as a service (IaaS)

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Entrans: Leveraging kinetic energy harvesting signal for transportation mode detection

arXiv

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arXiv 2018年

作者： Lany, Guohao Xu, Weitao Ma, Dong Khalifa, Sara Hassan, Mahbub Hu, Wen Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States College of Computer Science and Software Engineering Shenzhen University China School of Computer Science and Engineering University of New South Wales Sydney Australia Distributed Sensing Systems research group Data61 CSIRO Australia

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, as well as providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 hours of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Accelerometers

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MoDeNA: Enhancing user security for devices in wireless personal and local area networks 11th

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11th IFIP WG 6.6 International Conference on Autonomous Infrastructure, Management, and Security, AIMS 2017

作者： Müller, Robert Waldvogel, Marcel Schmitt, Corinna Distributed Systems Laboratory Department of Computer and Information Science University of Konstanz Konstanz78457 Germany Communication Systems Group CSG Department of Informatics IfI University of Zurich UZH Binzmühlestrasse 14 Zurich8050 Switzerland

ISBN: (纸本)9783319607733

Today most used devices are connected with each other building the Internet of Things (IoT). A variety of protocols are used depending on the underlying network infrastructure, application (e.g., Smart City, eHealth), and device capability. The judgment of the security feeling of the data sharing depends on personal settings (e.g., easy to use, encrypted transmission, anonymization support). MoDeNA – a Mobile Device Network Assistant – was developed offering an opportunity for understanding the judgment of security by bringing the user’s concerns and their technology understanding of used devices and protocols into relation. MoDeNA provides a transparent overview over the used wireless security of the user’s device giving concrete advices for improving the connection security and usability of mobile device security. © The Author(s) 2017.

关键词： Network security

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FPGA-Accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-Off

FPGA-Accelerated Dense Linear Machine Learning: A Precision-...

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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Kaan Kara Dan Alistarh Gustavo Alonso Onur Mutlu Ce Zhang Department of Computer Science Systems Group ETH Zurich Switzerland Distributed Algorithms and Systems IST Austria

Stochastic gradient descent (SGD) is a commonly used algorithm for training linear machine learning models. Based on vector algebra, it benefits from the inherent parallelism available in an FPGA. In this paper, we first present a single-precision floating-point SGD implementation on an FPGA that provides similar performance as a 10-core CPU. We then adapt the design to make it capable of processing low-precision data. The low-precision data is obtained from a novel compression scheme-called stochastic quantization, specifically designed for machine learning applications. We test both full-precision and low-precision designs on various regression and classification data sets. We achieve up to an order of magnitude training speedup when using low-precision data compared to a full-precision SGD on the same FPGA and a state-of-the-art multi-core solution, while maintaining the quality of training. We open source the designs presented in this paper.

关键词： Field programmable gate arrays Quantization (signal) Training Stochastic processes Hardware Machine learning algorithms Data models

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