Submitted projects in full

  • QA in distributed cloud architecture: injection-fault testing

    Project description:

    Clients of the sync&share system (CERNBOX) are particularly exposed to "operational failures" due to heterogeneity of hardware, OS and network environments. 

    Sync&share system operates in very heterogenous network environment: from fast, reliable network inside the computing center to unreliable, high-latency ad-hoc connections such as from air-ports etc. 
    Windows filesystems have substantially different semantics (e.g. locking) from Unix filesystems -- these difference affect the synchronization process 
    the goal of the R&D is to analyze the environment and identify the relevant classes of failures in order to provide a reproducible framework for injecting faults at the system level for testing client-server data transmission 
    examples: 
    * network slowdown or packet loss 
    * local disk failure 
    * checksum errors 
    * failed software upgrades 
    the work is supported by real monitoring and logging data: failure patterns in an existing service (CERNBOX) 
    the work extends on existing testing framework (smashbox) 

    Project duration:
    6 months
    Contact for further details:
    massimo.lamanna@cern.ch
    Learning experience:
    Large-scale testing on a highlity non-homogeneous environemt (1,000s of concurrent clients, 10% of mobile clients (iOS and Android), Mac, Linux and Windows synch clients)
    Required skills:
    The technical competencies required are the knowledg the Python language. Knowledge of JavaScript and of tools like Dropbox, OwnCloud and Unison would be an important asset.
    Project area:
    Data Management
    Reference to the project tracker:
    CERN group:
    CERN IT-DSS
    Status:
    Submitted
  • QA in distributed cloud architecture: evolution of smashbox framework

    Project description:

    Cloud synchronization and sharing is an area in evolution with innovative services being built on top of different platforms. CERNBOX is a service ran at CERN to provide at the same time synchronisation services (based on the OwnCloud software) and high-performance data access and sharing (based on EOS, the CERN disk storage system for large-scale physics data analysis).

    The Smashbox framework (https://github.com/cernbox/smashbox) is successfully used on Linux clients to test OwnCloud/CERNBOX installations. The plans to extend it require to port it to non-Linux platforms:
    * Smashbox port to Windows platforms 
    * Smashbox port to Android 
    * Smashbox port to iOS 
    * Smashbox orchestration (concurrent execution across platforms)

    References:
    Project duration:
    3-12 months depending on the agreed scope
    Contact for further details:
    massimo.lamanna@cern.ch
    Learning experience:
    Testing, distributed data management, cloud storage
    Required skills:
    languages: python, operating systems: at least one among windows, iOS, Android
    Project area:
    Data Management
    Reference to the project tracker:
    CERN group:
    Status:
    Submitted
  • File Transfer Service (FTS) extensions

    Project description:

    The File Transfer Service (FTS) manages the global distribution
    of LHC data, moving multiple petabytes per month during a run and underpinning the whole data lifecycle. Join the FTS team in their development of this critical service. Possible projects include

    • authorised proxy sharing: allowing a production service to delegate a proxy and authorising others to use it
    • incorporation of support for new types of endpoint, for example cloud or archival storage
    Project duration:
    From 3 months, depending on task selected
    Contact for further details:
    oliver.keeble@cern.ch
    Learning experience:
    This project offers the chance to become involved with one of the critical data management systems used in computing for LHC and
    Required skills:
    C++/Linux, Python
    Project area:
    Data Management
    Reference to the project tracker:
    CERN group:
    IT-SDC
    Status:
    Submitted
  • Dynamic storage federations

    Project description:

    The group runs a project whose goal is the dynamic federation of

    • HTTP based storage systems, allowing a set of globally distributed resources to be integrated and appear via a single entry point. The task is to work on the development of this project (“dynafed”), implementing functional and performance extensions, in particular 
    • Redirection monitoring, to allow the logging of federator behaviour for real-time monitorng and subsequent analytics
    • Metadata integration, beginning with the incorporation of space usage information, allowing the federator to expose grid-wide storage metrics
    • An endpoint status/management subsystem. The basic feature would be an interface that publishes endpoint status (up/down/degraded). Management functions could also be incorporated, including ways to add/enable/disable endpoints without having to restart the service.
    • Semantic enhancements to the embedded rule-based authorization implementation, including turning the authorization subsystem into a pluggable authorization manager.
    • Deployment tests and development with other Apache security plugins, to support natively Identity Federations, like the CERN SSO, Facebook, Google and others. May benefit from the previous points about authorization.
    • Integration with experiment catalogues to benefit from available metadata and replica placement information.
    Project duration:
    From 3 to 9 months depending on a selected task
    Contact for further details:
    oliver.keeble@cern.ch
    Learning experience:
    Thisproject offers experience in how advanced, distributed storage systems are being used to handle the peta-scale data requirem
    Required skills:
    C++/Linux
    Project area:
    Data Management
    Reference to the project tracker:
    CERN group:
    IT-SDC
    Status:
    Submitted
  • Cloud data analysis

    Project description:

    Cloud synchronization and sharing is a promising area for the preparation of powerful transformative data services. 

    The goal of this project is to prepare CERNBOX to be used in connection with heavy-duty activities (large-scale batch processing) on the current LXBATCH infrastructure (LSF) and on its future evolution (HT-Condor): physicists can enable their data to move across their private workstations (like a private laptop) while the bulk of the data is directly accessed from the EOS infrastructure. At the same time, users can control the progress of their activity via mobile clients (as a smartphone) via optimised client applications or via standard browsers.
    The student will participate to the preparation and validation of these use cases. The student will participate to the deployment of the necessary infrastructure (EOS Fuse access from interactive and batch services), support the alpha users (physicists) and extend the current testing and validation system to these new use cases and to new platforms (acceptance tests – in connection with other sites running CERNBOX and monitoring – using the CERN monitoring infrastructure).

    An additional use case is the enabling of data viewers (ROOT tuple) in connection with the SFT team to allow seamless access to summary data (like histograms) from the CERNBOX portal directly.

    References:
    Project duration:
    6-12 months
    Contact for further details:
    massimo.lamanna@cern.ch
    Learning experience:
    Exposure to innovative techniques in cloud data analysis
    Required skills:
    The technical competencies required are the knowledge of the PHP or the Python languages. Knowledge of JavaScript and of tools like Dropbox, OwnCloud and Unison would be an important asset.
    Project area:
    Data Management
    Reference to the project tracker:
    CERN group:
    CERN IT-DSS
    Status:
    Submitted
  • Performance optimization in a High Throughput Computing environment

    Project description:

    Profiling of computing resources respect to WLCG experiment workloads is a crucial factor to select the most effective resources and to be able to optimise their usage.
    There is a rich amount of data collected by the CERN and WLCG monitoring infrastructures just waiting to be turned into useful information. This data covers all the areas of the computing activity such as (real and/or virtual) machine monitoring, storage, network, batch system performance, experiment job monitoring.
    Data gathered by those systems contain great intrinsic value, however information needs to be extracted and understood through a predictive data analytics process. The final purpose of this process is to support decisions and improve the efficiency and the reliability of the related services.
    For instance, with the adoption of the remote access of data it becomes mandatory to understand the impact of this approach to the job efficiency. Here the interplay of network and CPU effects, as well as the resource usage from multi VOs needs to be studied and understood. An interesting topic of study is the performance of job processing at the WLCG distributed T0 center, which is physically split between Computer Centers in Meyrin and Wigner. The goal of the project will be to understand the difference in the performance and to suggest possible optimization.

    The work will be conducted in close contact with the experts (CERN analytics working group, system managers, developers) and will provide a deep insight into the computing infrastructure of a WLCG datacenter, its design, technical requirements and operational challenges.

    References:
    Project duration:
    6 to 12 months
    Contact for further details:
    julia.andreeva@cern.ch
    Learning experience:
    Using analytics approaches already consolidated in other scientific domains, such as physics and finance, the candidate will learn and adopt techniques for data mining (trend analysis, result visualization, forecasting and predictive modeling) using cutting edge tools such as the analytics python ecosystem (IPython, numpy, matplotlib, scipy, pandas, scikit-learn, etc).
    Required skills:
    Python, matplotlib. Some experience in data analysis and statistics would be an advantage.
    Project area:
    Data Analytics
    Reference to the project tracker:
    CERN group:
    IT-SDC
    Status:
    Submitted
  • Distributed storage systems for big data

    Project description:

    The group maintains a framework called dmlite which is used to integrate various types of storage with different protocol frontends. It is the basis of a number of the group’s products such as the Disk Pool Manager (DPM), a grid storage system which holds over 50PB of storage in the global infrastructure. DPM/dmlite extensions
    The task is to contribute to the dmlite project by working on functional extensions to the framework. Example projects include

    • Exposing system data through a “procfs” style plugin
    • Incorporation of new AA mechanisms, eg outh
    • Creation of a web admin interface
    • Work on draining and file placement within the system
    • dmliteSE

    Help to realise the group's vision of a “dmliteSE” by working on the gradual retirement of legacy daemons within the DPM system. In this context, tackle the modernisation of pool management and file placement, and the incorporation of new resource types (eg cluster file systems) into the system. Complete the functional development required to allow operation of a disk storage system purely through standard protocols.

    Project duration:
    3 to 9 months depending on the selected task
    Contact for further details:
    oliver.keeble@cern.ch
    Learning experience:
    This project offers the chance to become involved with one of the storage systems used in computing for LHC and will give an opp
    Required skills:
    C++/Linux
    Project area:
    Data Management
    Reference to the project tracker:
    CERN group:
    IT-SDC
    Status:
    Submitted
  • Advanced Notifications for WAN Incidents

    Project description:

    One of the main challenges in WLCG WAN networking is the network diagnostics and advanced notifications on the issues seen in the network. LHCOPN/LHCONE as the core global networks in WLCG have more than 5000 active links between 120 sites. Currently, most of the issues are only visible by the applications and need to be debugged after the incident and performance degradation has already occurred. This is primarily due to the underlying complexity of the WLCG network (multi-domain) and difficulty to understand state of the network and how it changes over time. The project will aim to use the current open-source event processing systems to automate detection and location of the network problems using the existing data from the perfSONAR network infrastructure. The project will be done in collaboration with University of Chicago and University of Michigan.

    The project will build on the standard WLCG perfSONAR network measurement infrastructure and will aim to gather and analyze complex real-world network topologies and their corresponding network metrics to identify possible signatures of the network problems. It will provide a real-time view on the existing diagnosed issues together with a list of existing downtimes from the network providers to the experiments operations teams.

     

    Project duration:
    12 months
    Contact for further details:
    Marian.Babik@cern.ch
    Learning experience:
    The student will acquire practical experience in machine learning, event stream processing as well as software engineering and container-based deployment and operations.
    Required skills:
    TCP/IP networking, Python, Machine learning
    Project area:
    Monitoring of the distributed infrastructure
    Reference to the project tracker:
    CERN group:
    IT/CM
    Status:
    Submitted
  • Advanced Notifications for WAN Incidents

    Project description:

    One of the main challenges in WLCG WAN networking is the network diagnostics and advanced notifications on the issues seen in the network. LHCOPN/LHCONE as the core global networks in WLCG have more than 5000 active links between 120 sites. Currently, most of the issues are only visible by the applications and need to be debugged after the incident and performance degradation has already occurred. This is primarily due to the underlying complexity of the WLCG network (multi-domain) and difficulty to understand state of the network and how it changes over time. The project will aim to use the current open-source event processing systems to automate detection and location of the network problems using the existing data from the perfSONAR network infrastructure. The project will be done in collaboration with University of Chicago and University of Michigan.

    The project will build on the standard WLCG perfSONAR network measurement infrastructure and will aim to gather and analyze complex real-world network topologies and their corresponding network metrics to identify possible signatures of the network problems. It will provide a real-time view on the existing diagnosed issues together with a list of existing downtimes from the network providers to the experiments operations teams.

     

    Project duration:
    12 months
    Contact for further details:
    Marian.Babik@cern.ch
    Learning experience:
    The student will acquire practical experience in machine learning, event stream processing as well as software engineering and container-based deployment and operations.
    Required skills:
    TCP/IP networking, Python, Machine learning
    Project area:
    Data Analytics
    Reference to the project tracker:
    CERN group:
    IT/CM
    Status:
    Submitted
  • Optimisation of experiment workflows in the Worldwide LHC Computing Grid

    Project description:

    The LHC experiments perform the vast majority of the data processing and analysis on the Worldwide LHC Computing Grid (WLCG), which provides a globally distributed infrastructure with more than 500k cores to analyse the tens of PB of data collected each year. Profiling of the computing infrastructure with respect to the impact of different workloads is a crucial factor to find  the most efficient match between resources and use cases.  From the current analysis it is clear that the efficiency is neither perfect nor well understood.

    There is a rich amount of information collected by the communities' monitoring infrastructures. The scale and complexity of this data presents an analytics challenge on its own. So far the full potential hasn't been exploited. This data covers all the areas of the computing activities such as host monitoring, storage, network, batch system performance, user level job monitoring. Extracting useful knowledge from this data requires the use of state of the art data analytics tools and processes. The final purpose is to gain deep understanding of what determines the efficiency and how it can be improved. 

    ElasticSearch is a distributed, search and analytics engine  that is used at CERN to store and process large amounts of monitoring data for several experiments.

    It has been noted that differences in data access patterns lead to significantly different utilisation of the resources. However, the concrete causes and quantitative relations are still badly understood. In the same way job failures due a variety of underlying causes lead to loss of productivity, without knowing the exact causes and the concrete scale of the different issues.  

    To be able to improve the overall efficiency we suggest to studying the dependency of the performance on a variety of variables. Based on these findings, which could be obtained by classical and/or machine learning based data analysis techniques, new strategies should be developed. Since the expected gains are on the order of 10-20% the outcome of this work is of great importance for the experiments and the infrastructure providers. 

    The work in this project will be done in close collaboration with experts from CERN IT and the LHC experiments. 

    References:
    Project duration:
    3 to 6 months
    Contact for further details:
    Andrea.Sciaba@cern.ch
    Learning experience:
    Large scale data analytics with real world data, understanding of different approaches to handle the processing of data at the PByte scale in a complex distributed environment. Python data analysis ecosystem (NumPy, pandas, SciPy, matplotlib, Jupyter). Direct interaction with members of the LHC collaborations and an insight into their computing systems.
    Required skills:
    Comfortable with Python programming. Some basic notion of statistics and probability.
    Project area:
    Data Analytics
    Reference to the project tracker:
    CERN group:
    IT-DI
    Status:
    Submitted
  • Analysis of the I/O performance of LHC computing jobs at the CERN computing centre

    Project description:

    The LHC experiments execute a significant fraction of their data reconstruction, simulation and analysis on the CERN computing batch resources. One of the most important features of these data processing jobs is their I/O pattern in accessing the local storage system, EOS, which is based on the xrootd protocol. In fact, the way experiment applications access the data can have a considerable impact on how efficiently the computing, storage and network resources are used, and has important implications on the optimisation and size of these resources.

    A promising approach is to study the logs of the storage system to identify and characterise the job I/O, which is strongly dependent on
    the type of jobs (simulation, digitisation, reconstruction, etc.). A direct link between the information in the storage logs and the information in the monitoring systems of the experiments (which contain detailed information about the jobs) is possible, as it can be derived from a cross analysis of the aforementioned data sources together with information from the CERN batch systems. The goal of this project is to study such connection, use it to relate I/O storage patterns to experiment job types, look for significant variations within a given job type, identify important sources of inefficiency and describe a simple model for the computer centre (batch nodes, network, disk servers) that would increase the efficiency of the resource utilisation.

    In case inefficiencies are detected that could be alleviated by changes in the way experiments run their jobs, this information should be passed to the experiments.

    The analysis can be initially based on the jobs of a single large LHC experiment (ATLAS or CMS) and extended to other experiments if time allows.

    References:
    Project duration:
    3 to 6 months
    Contact for further details:
    Andrea.Sciaba@cern.ch
    Learning experience:
    Large scale data analytics with real world data. Python data analysis ecosystem (NumPy, pandas, SciPy, matplotlib, Jupyter). Direct interaction with members of the LHC collaborations and an insight into their computing systems. Complex storage systems in a large data centre environment.
    Required skills:
    Python programming. Familiarity with data analytics techniques and tools is desirable.
    Project area:
    Data Analytics
    Reference to the project tracker:
    CERN group:
    Status:
    Submitted
  • e-learning - IT Collaboration, Devices & Applications - Indico Usability study

    Project description:

    Indico is an open source web application for event organization, archival and collaboration. It is developed at CERN and evolves in the  IT Collaboration, Devices & Applications (IT/CDA) group

    The application is used by tens of thousands of users around the world and across projects, universities, laboratories and UN agencies.

    The Room Booking module is under-going a major re-design that will change completely its look & feel.

    This project will measure user reaction and make recommendations to the module developers on its usability by:

    1. Taking a random sample of users (amongst IT members, secretariats, physicists, administrators,...).
    2. Make a list of issues and proposals for improvement this usability exercise revealed.
    3. Preparing the set-up for screen recording - face recording -voice recording when they navigate through the new interface of the module (via ActivePresenter, QuickTime &/or the CERN audiovisual services).
    4. Study relevant past studies (example) to understand whether an eye-tracking equipment loan or rental (TECFA or HUG or tobii) is useful for this exercise.
    5. Recording their sessions and analysing the videos, taking notes, making summary report.
    6. Make a list of issues this usability exercise revealed.

     

     

    References:

    Previous Usability study, different application, same collaborating institute: https://cds.cern.ch/record/2054880

    Related project: https://twiki.cern.ch/ELearning

    Collaborating institute: https://www.hesge.ch/heg/en/core-programmes/bachelors-science/information-studies (link is external)

    Project duration:
    6 months 2 days/week, often from home
    Contact for further details:
    Maria Dimou
    Learning experience:
    Thanks to the vast variety of users that need Indico and their very different profiles, the student will gain technically (set-up of the process and analysis of the results) and organisationally (emails, doodles, web pages, reports, contact with the developers).
    Required skills:
    Expertise in web usability, optimal web navigation patterns, organisational and reporting skills, some recording skills.
    Project area:
    Learning
    Reference to the project tracker:
    CERN group:
    IT-CDA
    Status:
    Submitted
  • Malt-related project: Standard documentation workflow and conversion tools for documentation, slides etc

    Project description:

    Prélude project

    1 August  - 15 September 2019:

    Develop a new search engine for Indico. Details here.

    Supervision by Pedro Ferreira <pedro.ferreira@cern.ch>.

    Main project

    In order to standardise the way service web sites (both for user facing and administrators pages) are handled across the group and department, IT/CDA proposes a standard way to create, maintain and serve service web sites, based on modern and open technologies (Markdown, Gitlab and Openshift). The process of creating such documentation web sites is  documented here.  The student will participate in the development of an easy-to-use workflow to simplify to the maximum the management of such web sites. Since currently many such pages are in existing Twiki, Sharepoint or Drupal documentation pages, the student will also write scripts to convert existing such pages in Markdown automatically.

    Some of our service documentation pages (printers, mail quota, personal storage space quota, certificate, password renewal) are dynamic. The student will have to port the scripts behind such dynamic pages to the new Markdown-based documentation.

    In addition, the student should deploy an operational solution for easy making and hosting of non Microsoft .pptx slides. The current recommendation https://twiki.cern.ch/Edutech/NonPowerPointSlides needs improvement to become operational documentation.

    References:
    1. Submitted for Technical Students' selection in the autumn 2018 but refused.
    2. Re-submitted for the May 2019 Tech. Stud. selection and approved. Selected in smartrecruiters on 2019-04-29:
    Project duration:
    1 year
    Contact for further details:
    Maria Dimou
    Learning experience:
    The IT/CDA group hosts a big amount of important communication, documentation, web and computing services https://cern.ch/it-dep-cda/services/. Getting the experience of these tools will be valuable for the student's future professional life.
    Required skills:
    Programming and good english. Good communication skills for exchanges with the service managers.
    Project area:
    Data Management
    Reference to the project tracker:
    CERN group:
    IT-CDA
    Status:
    Submitted
  • Malt-related project: Standard documentation workflow and conversion tools for documentation, slides etc

    Project description:

    Prélude project

    1 August  - 15 September 2019:

    Develop a new search engine for Indico. Details here.

    Supervision by Pedro Ferreira <pedro.ferreira@cern.ch>.

    Main project

    In order to standardise the way service web sites (both for user facing and administrators pages) are handled across the group and department, IT/CDA proposes a standard way to create, maintain and serve service web sites, based on modern and open technologies (Markdown, Gitlab and Openshift). The process of creating such documentation web sites is  documented here.  The student will participate in the development of an easy-to-use workflow to simplify to the maximum the management of such web sites. Since currently many such pages are in existing Twiki, Sharepoint or Drupal documentation pages, the student will also write scripts to convert existing such pages in Markdown automatically.

    Some of our service documentation pages (printers, mail quota, personal storage space quota, certificate, password renewal) are dynamic. The student will have to port the scripts behind such dynamic pages to the new Markdown-based documentation.

    In addition, the student should deploy an operational solution for easy making and hosting of non Microsoft .pptx slides. The current recommendation https://twiki.cern.ch/Edutech/NonPowerPointSlides needs improvement to become operational documentation.

    References:
    1. Submitted for Technical Students' selection in the autumn 2018 but refused.
    2. Re-submitted for the May 2019 Tech. Stud. selection and approved. Selected in smartrecruiters on 2019-04-29:
    Project duration:
    1 year
    Contact for further details:
    Maria Dimou
    Learning experience:
    The IT/CDA group hosts a big amount of important communication, documentation, web and computing services https://cern.ch/it-dep-cda/services/. Getting the experience of these tools will be valuable for the student's future professional life.
    Required skills:
    Programming and good english. Good communication skills for exchanges with the service managers.
    Project area:
    Learning
    Reference to the project tracker:
    CERN group:
    IT-CDA
    Status:
    Submitted
  • MAlt: Preparing the new CERN telephony service

    Project description:

    The CERN telephony landscape is going to experience major changes in the coming two years with a new fully open IP telephony system currently in development in IT.

    The student will be involved in various aspects of the preparation of this new service: adapting the existing provisioning interfaces and processes to the new context both for phone numbers themselves and for IP hardware phones, adapting the lifecycle management processes for phone numbers, developping a central dashboard for monitoring the status of the service infrastructure, writing and implementing test cases for the various phone clients in development.

    References:
    Project duration:
    1 to 2 years
    Contact for further details:
    thomas.baron@cern.ch
    Learning experience:
    Joining a dynamic project team on challenging developments, touching many modern technology stacks, contributing in many areas of the development of a central communication service.
    Required skills:
    Web development (Flask, HTML, JS, React) Mobile development (React Native) Python Docker Openshift
    Project area:
    Data Management
    Reference to the project tracker:
    CERN group:
    IT-CDA
    Status:
    Submitted
  • MAlt: Preparing the new CERN telephony service

    Project description:

    The CERN telephony landscape is going to experience major changes in the coming two years with a new fully open IP telephony system currently in development in IT.

    The student will be involved in various aspects of the preparation of this new service: adapting the existing provisioning interfaces and processes to the new context both for phone numbers themselves and for IP hardware phones, adapting the lifecycle management processes for phone numbers, developping a central dashboard for monitoring the status of the service infrastructure, writing and implementing test cases for the various phone clients in development.

    References:
    Project duration:
    1 to 2 years
    Contact for further details:
    thomas.baron@cern.ch
    Learning experience:
    Joining a dynamic project team on challenging developments, touching many modern technology stacks, contributing in many areas of the development of a central communication service.
    Required skills:
    Web development (Flask, HTML, JS, React) Mobile development (React Native) Python Docker Openshift
    Project area:
    Monitoring of the distributed infrastructure
    Reference to the project tracker:
    CERN group:
    IT-CDA
    Status:
    Submitted
  • Documentation Project - Migration of static web sites to Markdown

    Project description:

    The group Collaboration, Devices and Applications (CDA) in CERN IT Department initiated a Documentation Project for moving static service documentation web sites containing service documentation to Markdown. For this purpose, software tools are being developed, instructions being written and  pilot testers contacted.

    The student will have to:

    1. apply the instructions in order to create a new web site according to the policy for site naming, hosting, authoring.
    2. observe new-comer users' reactions to the procedure, the tools and the suggested rich-text editors.
    3. identify shortcomings in any area of point 2 above and report to the documentation-project members.
    4. document tips in form of FAQs and/or discourse articles.
    5. make recommendations to the documentation-project team for an optimal user experience.
    6. disseminate the instructions via alternative methos, namely online tutorials.
    7. convert some of the groups' own service documentations to test the process in operation.
    8. make video tutorials that explain the documentation migration/creation process.
    9. officialise the Instructions by turning them into a real Markdown documentation page, including migration of existing sites currently hosted in EOS to OpenShift.
    References:

    Documentation strategy presentation

    Project progress notes

    Project duration:
    6 months
    Contact for further details:
    Maria Dimou
    Learning experience:
    We are working with Open Source tools which are the best ethical and technical quality attitude to have in computing. The technical gain will be great for the student and the contact with the CERN IT experts and users will be very enriching.
    Required skills:
    Good english (spoken and written). Understanding Information Technology (IT) tools. Linux sophisticated user knowledge would be ideal. Good communication skills. The tasks require contact with many users and experts. Availability for an internship contract, at least 50% working time for 1-2 months, with possible extension up to 6 months. Internship salary is 1500CHF/month for a 100% contract (40hrs per week).
    Project area:
    Learning
    Reference to the project tracker:
    CERN group:
    IT-CDA
    Status:
    Submitted
  • Documentation Project - Fetch SNOW info into Markdown sites via the API

    Project description:

    The CERN IT group on Collaboration, Devices and Applications (CDA) looks, amongst a big amount of the services it provides, into documentation recommendations and tools.

    In these discussions, we decided to investigate importing from the CERN Service Portal the values of:

    1. the short description field of  IT CDA services' Service Elements (SEs) (or Functional Elements (FEs) when necessary). See background info here.
    2. the content of Knowledge Base articles to enhance FAQs of Markdown general documentation. See background info here.

    The objective is to write a script that synchronises the short service description with the relevant Service Now  (SNOW) SE or FE entry  value (point 1 above) and evaluate the advantages/disadvantages of doing 2.

    Example of point 1:

    The text in the centre of page https://cern.ch/it-dep-cda/services/e-learning should be identical (automatically fetched from SNOW into Jekyll when a SNOW update occurs.

    1. This is how to request a SNow API https://cern.service-now.com/service-portal/report-ticket.do?name=snow-api-access&se=servicenow-application-support
    2. The file lcoation of the above example https://gitlab.cern.ch/it-dep-cda/public-website/blob/master/_ic/e-learning.md
    3. The SNOW content of this service https://cern.service-now.com/service-portal/service-element.do?name=itelearningservice
    4. SNow expert is Jorge Garcia Cuervo.
    5. SNow API request example with authentication issues to solve  here.
    6. Manual report extraction method and results here. Doing this is needed to test the validity of data extracted programmatically.

                

     

    References:
    1. All documentation-related strategy meetings (most recent first) here.
    2. A development project on Documentation conversion tools (and more) here.
    3. An internship project for migrating existing static web sites to markdown here.
    Project duration:
    3 months
    Contact for further details:
    Maria Dimou
    Learning experience:
    Work in a team of skilled programmers and very busy people, meaning learning to find things on one's own. Learn to present the results in a brief and functional way to the project team and the service owners. Be part of the life of the world's biggest physics laboratory in an IT department with more than 300 experts.
    Required skills:
    Programming skills for web development, authentication/authorisation tools, REST APIs, testing, communication with content owners.
    Project area:
    Data Management
    Reference to the project tracker:
    CERN group:
    Status:
    Submitted
  • Documentation Project - Fetch SNOW info into Markdown sites via the API

    Project description:

    The CERN IT group on Collaboration, Devices and Applications (CDA) looks, amongst a big amount of the services it provides, into documentation recommendations and tools.

    In these discussions, we decided to investigate importing from the CERN Service Portal the values of:

    1. the short description field of  IT CDA services' Service Elements (SEs) (or Functional Elements (FEs) when necessary). See background info here.
    2. the content of Knowledge Base articles to enhance FAQs of Markdown general documentation. See background info here.

    The objective is to write a script that synchronises the short service description with the relevant Service Now  (SNOW) SE or FE entry  value (point 1 above) and evaluate the advantages/disadvantages of doing 2.

    Example of point 1:

    The text in the centre of page https://cern.ch/it-dep-cda/services/e-learning should be identical (automatically fetched from SNOW into Jekyll when a SNOW update occurs.

    1. This is how to request a SNow API https://cern.service-now.com/service-portal/report-ticket.do?name=snow-api-access&se=servicenow-application-support
    2. The file lcoation of the above example https://gitlab.cern.ch/it-dep-cda/public-website/blob/master/_ic/e-learning.md
    3. The SNOW content of this service https://cern.service-now.com/service-portal/service-element.do?name=itelearningservice
    4. SNow expert is Jorge Garcia Cuervo.
    5. SNow API request example with authentication issues to solve  here.
    6. Manual report extraction method and results here. Doing this is needed to test the validity of data extracted programmatically.

                

     

    References:
    1. All documentation-related strategy meetings (most recent first) here.
    2. A development project on Documentation conversion tools (and more) here.
    3. An internship project for migrating existing static web sites to markdown here.
    Project duration:
    3 months
    Contact for further details:
    Maria Dimou
    Learning experience:
    Work in a team of skilled programmers and very busy people, meaning learning to find things on one's own. Learn to present the results in a brief and functional way to the project team and the service owners. Be part of the life of the world's biggest physics laboratory in an IT department with more than 300 experts.
    Required skills:
    Programming skills for web development, authentication/authorisation tools, REST APIs, testing, communication with content owners.
    Project area:
    Learning
    Reference to the project tracker:
    CERN group:
    Status:
    Submitted
  • Documentation Project - Fetch SNOW info into Markdown sites via the API

    Project description:

    The CERN IT group on Collaboration, Devices and Applications (CDA) looks, amongst a big amount of the services it provides, into documentation recommendations and tools.

    In these discussions, we decided to investigate importing from the CERN Service Portal the values of:

    1. the short description field of  IT CDA services' Service Elements (SEs) (or Functional Elements (FEs) when necessary). See background info here.
    2. the content of Knowledge Base articles to enhance FAQs of Markdown general documentation. See background info here.

    The objective is to write a script that synchronises the short service description with the relevant Service Now  (SNOW) SE or FE entry  value (point 1 above) and evaluate the advantages/disadvantages of doing 2.

    Example of point 1:

    The text in the centre of page https://cern.ch/it-dep-cda/services/e-learning should be identical (automatically fetched from SNOW into Jekyll when a SNOW update occurs.

    1. This is how to request a SNow API https://cern.service-now.com/service-portal/report-ticket.do?name=snow-api-access&se=servicenow-application-support
    2. The file lcoation of the above example https://gitlab.cern.ch/it-dep-cda/public-website/blob/master/_ic/e-learning.md
    3. The SNOW content of this service https://cern.service-now.com/service-portal/service-element.do?name=itelearningservice
    4. SNow expert is Jorge Garcia Cuervo.
    5. SNow API request example with authentication issues to solve  here.
    6. Manual report extraction method and results here. Doing this is needed to test the validity of data extracted programmatically.

                

     

    References:
    1. All documentation-related strategy meetings (most recent first) here.
    2. A development project on Documentation conversion tools (and more) here.
    3. An internship project for migrating existing static web sites to markdown here.
    Project duration:
    3 months
    Contact for further details:
    Maria Dimou
    Learning experience:
    Work in a team of skilled programmers and very busy people, meaning learning to find things on one's own. Learn to present the results in a brief and functional way to the project team and the service owners. Be part of the life of the world's biggest physics laboratory in an IT department with more than 300 experts.
    Required skills:
    Programming skills for web development, authentication/authorisation tools, REST APIs, testing, communication with content owners.
    Project area:
    Data Analytics
    Reference to the project tracker:
    CERN group:
    Status:
    Submitted

You are here