Adding SMP support to fastpaths in an L4 microkernel

Fastpaths are a method of optimization which relies on treating the most commonly executed cases of certain functions in a privileged manner, such that behaviour is not modified, but execution time is reduced.Fastpaths play an important role on improving paravirtualization performance offered by an L4 microkernel.In this article we redesign two existing fastpaths in an L4 microkernel for the purpose of adding SMP support.We then put these fastpaths through a series of regression and performance tests to determine if the design is correct and what performance benefits we can expect by using them on a multiprocessor system

EOS workshop

Last year I have deployed EOS Citrine at RO-03-UPB for Alice experiment. I can share the steps and the issues I had. It would be a short presentation of lessons learns

Client Request Analysis Tool for CERN ALICE Grid Services

ALICE is an experiment hosted at the CERN facilities in Geneva. The data collected by the experiment is distributed in multiple geo-locations. ALICE software development team is managing the technologies that offer a unitary view to the distributed resources. AliEn API Services and JAliEn Services are used by the researchers to access and analyze the collected data. The ALICE developers cannot analyze the client-server interactions, while the only information available is gathered in the log files. The main requirement of the project is to provide an analysis tool to extract vital information out the log files. The tool is tested against log files generated by the ALICE production servers and is deployed on a custom ALICE-owned instance. As of June 2020, the project is being used by the developers to extract near real-time viable requests information and have a correct picture of the researcher’s interaction with the services

The Proposition and Evaluation of the RoEduNet-SIMARGL2021 Network Intrusion Detection Dataset

Cybersecurity is an arms race, with both the security and the adversaries attempting to outsmart one another, coming up with new attacks, new ways to defend against those attacks, and again with new ways to circumvent those defences. This situation creates a constant need for novel, realistic cybersecurity datasets. This paper introduces the effects of using machine-learning-based intrusion detection methods in network traffic coming from a real-life architecture. The main contribution of this work is a dataset coming from a real-world, academic network. Real-life traffic was collected and, after performing a series of attacks, a dataset was assembled. The dataset contains 44 network features and an unbalanced distribution of classes. In this work, the capability of the dataset for formulating machine-learning-based models was experimentally evaluated. To investigate the stability of the obtained models, cross-validation was performed, and an array of detection metrics were reported. The gathered dataset is part of an effort to bring security against novel cyberthreats and was completed in the SIMARGL project

Evaluation of multi-source downloads for FTS

The data transfer in the Grid at CERN (the European Organization for Nuclear Research) has seen constant improvement, be it through optimizing the existing tools, GridFTP and GFAL2 or by adding new tools such as XRootd. Unfortunately, all these have reached the maximum limit in terms of throughput. They are limited not by the network infrastructure, but by the fact that they use a single source for transfers, despite the existence of multiple replicas. In this paper we take on the challenge of evaluating the effects of using multiple sources, over the throughput, by comparing the download speed of the tools mentioned above with Aria2 and an under development version of XRootd, both supporting the use of multiple sources

Harnessing the Power of Threat Intelligence in Grids and Clouds: WLCG SOC Working Group

The modern security landscape affecting Grid and Cloud sites is evolving to include possible threats from a range of avenues, including social engineering as well as more direct approaches. An effective strategy to defend against these risks must include cooperation between security teams in different contexts. It is essential that sites have the ability to share threat intelligence data with confidence, as well as being able to act on this data in a timely and effective manner.As reported at ISGC 2017, the Worldwide LHC Computing Grid (WLCG) Security Operations Centres Working Group (WG) has been working with sites across the WLCG to develop a model for a Security Operations Centre reference design. This work includes not only the technical aspect of developing a security stack appropriate for sites of different sizes and topologies, but also the more social aspect of sharing data between groups of different kinds. In particular, since many Grid and Cloud sites operate as part of larger University or other Facility networks, collaboration between Grid and Campus / Facility security teams is an important aspect of maintaining overall security.We discuss recent work on sharing threat intelligence, particularly involving the WLCG MISP instance hosted at CERN. In addition, we examine strategies for the use of this intelligence, as well as considering recent progress in the deployment and integration of the Bro Intrusion Detection System (IDS) at contributing sites.An important part of this work is a report on the first WLCG SOC WG Workshop / Hackathon, a Workshop planned at time of writing for December 2017. This Workshop provides an opportunity to assist participating sites in the deployment of these security tools as well as giving attendees the opportunity to share experiences and consider site policies as a result. This Workshop is hoped to play a substantial role in shaping the future goals of the working group, as well as shaping future workshops

Cohesion network analysis: customized curriculum management in Moodle

Learning Management Systems frequently act as platforms for online content which is usually structured hierarchically into modules and lessons to ease navigation. However, the volume of information may be overwhelming, or only part of the lessons may be relevant for an individual; thus, the need for customized curricula emerges. We introduce a Moodle plugin developed to help learners customize their curriculum to best fit their learning needs by relying on specific filtering criteria and semantic relatedness. For this experiment, a Moodle instance was created for doctors working in the field of nutrition in early life. The platform includes 78 lessons tackling a wide variety of topics, organized into five modules. Our plugin enables users to specify basic filtering criteria, including their field of expertise, topics of interest from a predefined taxonomy, or expected themes (e.g., background knowledge, practice & counselling, or guidelines) for a preliminary pre-screening of lessons. In addition, learners can also provide a description in natural language of their learning interests. This text is compared with each lesson’s description using Cohesion Network Analysis, and lessons are selected above an experimentally set threshold. Our approach also takes into account prior knowledge requirements, and may suggest lessons for further reading. Overall, the plugin covers the management of the entire course lifecycle, namely: a) creating a customized curriculum; b) tracking the progress of completed lessons; c) generating completion certificates with corresponding CME points

Curricula customization with the readerbench Framework

Providing customized curricula tailored to learner's needs became a stringent problem while relating to the increasing number of people attending Massive Open Online Courses (MOOCs) and eLearning platforms because the same content is provided to all students. This study presents a Moodle plugin created on top of an eLearning course that enables curricula customization based on the learning needs of a high number of participants. With the help of the Mass Customization approach, two categories of attendees were identified in a previous research and imposed multiple filtering criteria, out of which the first one refers to participants� profession. The second criterion, topics of interest, allows learners to select keywords of interest from a predefined two-level word list, but also to enumerate their own terms using natural language. With the support of ReaderBench, an advanced Natural Language Processing framework, the most relevant lessons are retrieved in descending order of semantic relatedness. Third, an additional specific parameter allows participants to establish what kind of learning materials they require - i.e., theoretical and background oriented, practice and counseling documents, or guidelines. Our collection of documents is composed of lessons with a short description and their title, together with lists of pre- and post-requisite lessons. Our tool provides a comprehensive list of recommended lessons that best match the input criteria, corroborated with the list of related pre- and post-requisite lessons. Moreover, we provide information in terms of the duration of each lesson, as well as potential Continuous Medical Education points gained after finishing all selected lessons.<br/

Exposing the parton-hadron transition within jets with energy-energy correlators in pp collisions at s=5.02\sqrt{\textit s}=5.02 TeV

International audienceThis paper presents a fully-corrected measurement of the energy-energy correlator (EEC) within jets in pp collisions. The EEC traces the energy flow as a highly energetic parton undergoes a QCD shower followed by the confinement of partons into hadrons, probing the correlation function of the energy flow inside jets. The EEC observable is measured as a function of the charged particle pair angular distance, $R_{\rm L}$, for $20 < p_{\rm T}^{\rm ch \, jet} < 80$ GeV/$c$. In the perturbative region (large $R_{\rm L}$), a good agreement between the data and a next-to-leading-log perturbative QCD calculation is observed. In the non-perturbative region (small $R_{\rm L}$), the data exhibits a linear $R_{\rm L}$ dependence. There is a transition region in between, characterized by a turnover in the EEC distribution, corresponding to the confinement process. The peak of this transition region is located at $2.42 \pm 0.17$ GeV/$c/\langle p_{\rm T}^{\rm ch \, jet}\rangle$ for jets of various energies, indicating a common energy scale for the hadronization process. State-of-the-art Monte Carlo event generators are compared with the measurements, and can be used to constrain the parton shower and hadronization mechanisms