1,721,330 research outputs found
DiG: enabling out-of-band scalable high-resolution monitoring for data-center analytics, automation and control (extended)
Full text linkData centers are increasing in size and complexity, and we need scalable approaches to support their automated analysis and control. Performance counters and power consumption are their key “vital signs”. State-of-the-Art (SoA) monitoring systems provide built-in tools to collect performance measurements, and custom solutions to get insight on their power consumption. However, with the increase in measurement resolution (in time and space) and the ensuing huge amount of measurement data to handle, new challenges arise, such as bottlenecks on the network bandwidth, storage and software overhead on the monitoring units. To face these challenges we propose a novel monitoring platform for data centers, which enables real-time high-resolution profiling (i.e., all available performance counters and the entire signal bandwidth of the power consumption at the plug—sampling up to 20 μ s —with an error below 1%) and analytics, both at the edge (node-level analysis) and on a centralized unit (cluster-level analysis). The monitoring infrastructure is completely out-of-band, scalable, technology agnostic and low cost, and it is already installed in a SoA high-performance compute cluster (i.e., D.A.V.I.D.E. —18th in Green500 November 2017)
PAElla: Edge AI-Based Real-Time Malware Detection in Data Centers
Full text linkThe increasing use of Internet-of-Things (IoT) devices for monitoring a wide spectrum of applications, along with the challenges of 'big data' streaming support they often require for data analysis, is nowadays pushing for increased attention to the emerging edge computing paradigm. In particular, smart approaches to manage and analyze data directly on the network edge, are more and more investigated, and artificial intelligence (AI)-powered edge computing is envisaged to be a promising direction. In this article, we focus on data centers (DCs) and supercomputers (SCs), where a new generation of high-resolution monitoring systems is being deployed, opening new opportunities for analysis like anomaly detection and security, but introducing new challenges for handling the vast amount of data it produces. In detail, we report on a novel lightweight and scalable approach to increase the security of DCs/SCs, which involves AI-powered edge computing on high-resolution power consumption. The method-called pAElla-targets real-time malware detection (MD), it runs on an out-of-band IoT-based monitoring system for DCs/SCs, and involves power spectral density of power measurements, along with autoencoders. Results are promising, with an F1-score close to 1, and a false alarm and malware miss rate close to 0%. We compare our method with State-of-the-Art (SoA) MD techniques and show that, in the context of DCs/SCs, pAElla can cover a wider range of malware, significantly outperforming SoA approaches in terms of accuracy. Moreover, we propose a methodology for online training suitable for DCs/SCs in production, and release open data set and code
Online Anomaly Detection in HPC Systems
Full text linkReliability is a cumbersome problem in High Performance Computing Systems and Data Centers evolution. During operation, several types of fault conditions or anomalies can arise, ranging from malfunctioning hardware to improper configurations or imperfect software. Currently, system administrator and final users have to discover it manually. Clearly this approach does not scale to large scale supercomputers and facilities: automated methods to detect faults and unhealthy conditions is needed. Our method uses a type of neural network called autoencoder trained to learn the normal behavior of a real, in-production HPC system and it is deployed on the edge of each computing node. We obtain a very good accuracy (values ranging between 90% and 95%) and we also demonstrate that the approach can be deployed on the supercomputer nodes without negatively affecting the computing units performance
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Full text linkWe 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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