1,720,988 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Performance and resource management in multi-virtualized clouds
No full textLe cloud computing permet aux entreprises de réduire la barrière et les coûts d'utilisation de l'informatique, en mutualisant les besoins avec d'autres utilisateurs. Cette mutualisation est permise par la technologie de la virtualisation. Il s'agit de vendre les ressources physiques, concrètes, d'un centre d'hébergement, comme des ressources virtuelles, logiques. L'efficacité de la solution de virtualisation, sur différents critères, est au coeur des préoccupations à la fois des fournisseurs de cloud, et des clients. Les premiers veulent servir le plus grand nombre de clients possibles avec les ressources physiques déjà disponibles. Il faut donc que la solution de virtualisation permette d'allouer les ressources sans gaspillage. Une autre cible d'optimisation est la consommation énergétique du centre d'hébergement, très impactée par cette bonne gestion des ressources. Les clients quant à eux, recherchent des garanties de performances et de prédictibilité de celles-ci : la solution de virtualisation doit offrir le même niveau de performance malgré l'abstraction des ressources physiques en ressources virtuelles. L'économie financière de l'utilisation du cloud est aussi une priorité. Les deux solutions de virtualisation principales sont les machines virtuelles et les conteneurs. Elles ont chacune leurs avantages et leurs inconvénients sur les axes de la gestion des ressources et des performances. Mais il est possible d'imbriquer les conteneurs dans les machines virtuelles, produisant ainsi un cloud multi-virtualisé. Comment alors profiter au mieux des caractéristiques des deux solutions dans ce nouvel environnement ? Cette thèse explore les problématiques qui émergent à la combinaison de ces deux solutions, et propose des systèmes pour obtenir avec la multi-virtualisation de meilleures performances, une gestion des ressources approfondie et un coût réduit. Elle décrit premièrement, une méthode de consolidation novatrice de la charge de travail pour aller plus loin dans la réduction de la consommation énergétique ; deuxièmement, un algorithme d'allocation des ressources aux conteneurs pour corriger leur problème inhérent de prédictibilité des performances ; et troisièmement, deux systèmes joints pour l'optimisation du réseau multi-virtualisé afin d'en améliorer les performances et l'utilisation des ressources, ainsi que de réduire le coût du cloud pour le client. Ces travaux agissent à tous les niveaux de la virtualisation imbriquée afin de propulser plus avant la technologie de la multi-virtualisation.Companies use cloud computing to lower entry and usage costs of using information technologies as a resource. The main feature of cloud computing that enables these lower costs is the pooling of resources with other users. Pooling of resources is based on virtualization. The principle is to sell physical, concrete resources from a data-center as virtual, abstract resources. The core concern for both cloud providers and clients is the efficiency, on various axes, of the virtualization solution. The former wish to serve as many clients as possible with the given physical resources of the data-center. It puts an emphasis on the capability of the virtualization solution to allocate resources with limited waste. Another target for optimization is the power usage of the data-center. Efficient management of resources has a great effect on it. As for clients of cloud computing, they seek guarantees on performance, including predictability. Indeed, the virtualization solution must provide them with the same performance level despite abstraction of physical resources into virtual resources. Buying cloud resources as cheap as possible is also a priority. There are two main virtualization solutions: virtual machines and containers. Both have their own sets of benefits and drawbacks, on the axes of resource management and performance. However containers can be nested inside of virtual machines, thus building a multi-virtualized cloud. How best to use both solutions in this new environment? This thesis explores issues that arise from combining both virtualization solutions. It proposes new systems to gain better performance, to improve resource management and to provide cheaper cloud services using multi-virtualization. First, it describes a novel workload consolidation method to further reduce power usage ; second, an allocation algorithm for resources of containers that fixes their intrinsic issue of performance predictability ; and third, two joint systems that optimize multi-virtualized networking to improve performance and resource utilization, and save money on cloud usage. Theses works play at every level of nested virtualization in order to move the technology of multi-virtualization forward
FaaSLoad : mesures fines de performance et de ressources pour le Function-as-a-Service
No full textCloud computing relies on a deep stack of system layers: virtual machine, operating system, distributed middleware and language runtime. However, those numerous, distributed, virtual layers prevent any low-level understanding of the properties of FaaS applications, considered as programs running on real hardware. As a result, most research analyses only consider coarse-grained properties such as global performance of an application, and existing datasets include only sparse data.FaaSLoad is a tool to gather fine-grained data about performance and resource usage of the programs that run on Function-as-a-Service cloud platforms. It considers individual instances of functions to collect hardware and operating-system performance information, by monitoring them while injecting a workload. FaaSLoad helps building a dataset of function executions to train machine learning models, studying at fine grain the behavior of function runtimes, and replaying real workload traces for in situ observations. This research software project aims at being useful to cloud system researchers with features such as guaranteeing reproducibility and correctness, and keeping up with realistic FaaS workloads. Our evaluations show that FaaSLoad helps us understanding the properties of FaaS applications, and studying the latter under real conditions
Performance et gestion de ressources dans un cloud multi-virtualisé
No full textCompanies use cloud computing to lower entry and usage costs of using information technologies as a resource. The main feature of cloud computing that enables these lower costs is the pooling of resources with other users. Pooling of resources is based on virtualization. The principle is to sell physical, concrete resources from a data-center as virtual, abstract resources. The core concern for both cloud providers and clients is the efficiency, on various axes, of the virtualization solution. The former wish to serve as many clients as possible with the given physical resources of the data-center. It puts an emphasis on the capability of the virtualization solution to allocate resources with limited waste. Another target for optimization is the power usage of the data-center. Efficient management of resources has a great effect on it. As for clients of cloud computing, they seek guarantees on performance, including predictability. Indeed, the virtualization solution must provide them with the same performance level despite abstraction of physical resources into virtual resources. Buying cloud resources as cheap as possible is also a priority. There are two main virtualization solutions: virtual machines and containers. Both have their own sets of benefits and drawbacks, on the axes of resource management and performance. However containers can be nested inside of virtual machines, thus building a multi-virtualized cloud. How best to use both solutions in this new environment? This thesis explores issues that arise from combining both virtualization solutions. It proposes new systems to gain better performance, to improve resource management and to provide cheaper cloud services using multi-virtualization. First, it describes a novel workload consolidation method to further reduce power usage ; second, an allocation algorithm for resources of containers that fixes their intrinsic issue of performance predictability ; and third, two joint systems that optimize multi-virtualized networking to improve performance and resource utilization, and save money on cloud usage. Theses works play at every level of nested virtualization in order to move the technology of multi-virtualization forward.Le cloud computing permet aux entreprises de réduire la barrière et les coûts d'utilisation de l'informatique, en mutualisant les besoins avec d'autres utilisateurs. Cette mutualisation est permise par la technologie de la virtualisation. Il s'agit de vendre les ressources physiques, concrètes, d'un centre d'hébergement, comme des ressources virtuelles, logiques. L'efficacité de la solution de virtualisation, sur différents critères, est au coeur des préoccupations à la fois des fournisseurs de cloud, et des clients. Les premiers veulent servir le plus grand nombre de clients possibles avec les ressources physiques déjà disponibles. Il faut donc que la solution de virtualisation permette d'allouer les ressources sans gaspillage. Une autre cible d'optimisation est la consommation énergétique du centre d'hébergement, très impactée par cette bonne gestion des ressources. Les clients quant à eux, recherchent des garanties de performances et de prédictibilité de celles-ci : la solution de virtualisation doit offrir le même niveau de performance malgré l'abstraction des ressources physiques en ressources virtuelles. L'économie financière de l'utilisation du cloud est aussi une priorité. Les deux solutions de virtualisation principales sont les machines virtuelles et les conteneurs. Elles ont chacune leurs avantages et leurs inconvénients sur les axes de la gestion des ressources et des performances. Mais il est possible d'imbriquer les conteneurs dans les machines virtuelles, produisant ainsi un cloud multi-virtualisé. Comment alors profiter au mieux des caractéristiques des deux solutions dans ce nouvel environnement ? Cette thèse explore les problématiques qui émergent à la combinaison de ces deux solutions, et propose des systèmes pour obtenir avec la multi-virtualisation de meilleures performances, une gestion des ressources approfondie et un coût réduit. Elle décrit premièrement, une méthode de consolidation novatrice de la charge de travail pour aller plus loin dans la réduction de la consommation énergétique ; deuxièmement, un algorithme d'allocation des ressources aux conteneurs pour corriger leur problème inhérent de prédictibilité des performances ; et troisièmement, deux systèmes joints pour l'optimisation du réseau multi-virtualisé afin d'en améliorer les performances et l'utilisation des ressources, ainsi que de réduire le coût du cloud pour le client. Ces travaux agissent à tous les niveaux de la virtualisation imbriquée afin de propulser plus avant la technologie de la multi-virtualisation
FaaSLoad: Fine-Grained Performance and Resource Measurement for Function-As-a-Service
Get PDFInternational audienceCloud computing relies on a deep stack of system layers: virtual machine, operating system, distributed middleware and language runtime. However, those numerous, distributed, virtual layers prevent any low-level understanding of the properties of FaaS applications, considered as programs running on real hardware. As a result, most research analyses only consider coarse-grained properties such as global performance of an application, and existing datasets include only sparse data. FaaSLoad is a tool to gather fine-grained data about performance and resource usage of the programs that run on Function-as-a-Service cloud platforms. It considers individual instances of functions to collect hardware and operating-system performance information, by monitoring them while injecting a workload. FaaSLoad helps building a dataset of function executions to train machine learning models, studying at fine grain the behavior of function runtimes, and replaying real workload traces for in situ observations. This research software project aims at being useful to cloud system researchers with features such as guaranteeing reproducibility and correctness, and keeping up with realistic FaaS workloads. Our evaluations show that FaaSLoad helps us understanding the properties of FaaS applications, and studying the latter under real conditions
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