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
  • e-learning - video production and Academic Training video archive promotion

    Project description:

    The Academic Training (AT) video archive in CDS contains a wealth of knowledge that we could promote in youtube as part of CERN's mission around Education.

    The plan, agreed with the CERN Academic Training Committee (ATC), CERN Communications experts and the IT/CDA management, is to make our corpus of recorded AT lectures more widely known via a dedicated CERNAcademicTraining YouTube channel. To prepare:

    1. Check other such sites on the web, e.g. NASA, Fermilab, Argonne, ESA, EPFL, UniGe, google, etc - also some sites of famous art institutions - and write down what we can learn from the best ones. Done, see result HERE.
    2. The ATC members and lecture series' sponsors to select 'best-of' past series in CDS, to use as pilot entries, classify them per discipline domain and equip them with keywords that will help web searched in the future. Done, as far as pilot entries is concerned. Keywords are still to come in. See result HERE.
    3. Propose content for a corporate wrapping of each lecture in youtube. Done. ATC and CERN Communications opted for the CERN logo clip as per other CERN youtube channels, instead of tailored AT  - see example HERE. Original idea to ask the CERN communications team to make a few seconds' teaser to place at the entry of the future youtube channel is not exactly abandoned, but we shouldn't wait due to the wrapper. If done, it can be inspired by TEDx talks' introduction, where keywords are flying around in music to introduce the topic.
    4. Use an automated tool to wrap existing CDS video records in the 'corporate' wrapper. IT/CDA-IC recently made a tool for slides' inclusion but not for short video clips, as suggested in the point above.
    5. Prepare an (ffmpeg-based) script (in IT/CDA-IC) that will merge the channels (mostly show the slides, with periodical intersection of the lecturer's face) in order for the existing CDS records to be optimally displayed in youtube. This is not there yet.
    6. Make CDS video playlists per domain so they can be fed into youtube as such. This is not there yet, in CDS functionality but planned for the next release, later in 2017. Playlists can be made in youtube already now, of course. Ideas for playlists: All about netrinos, the Higgs, medical applications, statistics, Machine Learning, Supercomputing, Computer Security,... etc.

    Project initiator/coordinator: Maria Dimou / Academic Training Committee chairperson and IT e-learning project leader.

    Information on the collaborating unit within Geneva University:

     

    http://www.unige.ch/fapse/faculte/organisation/tecfa/
    Director: Mireille Bétrancourt
    Professor: DanieK. Schneider
    This activity is related to master programme MALTT http://tecfa.unige.ch/maltt

    References:

    A COAS request in this index page with request date 07/11/2016.

    Project duration:
    6 months
    Contact for further details:
    Maria Dimou
    Learning experience:
    This project requires both pedagogical and technical skills. The student will work as an editor and advisor, with the CERN Communications' team and the the Academic Training sponsors, to emphasise the interesting points, while respecting the historical content. Then he will need technical skills to do the video editing. Experience from interactions with users and educational material content owners, as well as the documentation and presentation of the results will be gained. Formal notification techniques of the conclusions drawn from data patterns observed in the video parametres of our archived AT lectures.
    Required skills:
    Good video and text editing. Modern education and documentation management knowledge.
    Project area:
    Learning
    Reference to the project tracker:
    CERN group:
    IT-DI
    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
  • e-learning - IT Collaboration, Devices & Applications - ffmpeg

    Project description:

    The CERN Document Server (CDS) is the institutional repository of CERN publications, photos and videos, organised in Collections. Specifically videos of CERN lectures are recorded in two distinct channels, results of the lecturer and material captures.

    This project aims at exploiting the power of the tool ffmpeg, to edit, split and merge video channels in the Academic Training video collection in CDS. The result of this work will be a new unique video with lecturer/material periodic flip, or small lecturer image embedded in the material image. It will be used for uploading to a dedicated YouTube channel for CERNAcademicTraining. See parent project here (steps 4 & 5).

    The objective is to write a script that:

    1. Selects the right ffmpeg command options to swap channels in existing videos, e.g. between the lecturer and material channels. See an Example here. One can click on the video camera icon and see the speaker only, or the landscape icon and see the slides only.
    2. Allows the collection owner, as end-user, to easily define time-intervals when the video channels can be inverted or mixed, e.g. display a small-size lecturer within the material channel.

    In addition, the project requires testing the script, getting feedback from the Academic Training Committee (ATC) and storing it in the official CERN storage system (EOS) in the e-learning project area.

    Project duration:
    6 months
    Contact for further details:
    Maria Dimou
    Learning experience:
    Devising methods to promote the most attractive aspects from existing recordings of complex scientific talks. In terms of computing benefits: ffmpeg is an open and powerful tool. Knowing its internals is an advantage for one's CV.
    Required skills:
    Shell scripting, video editing, communication skills in a relatively large heterogeneous team.
    Project area:
    Learning
    Reference to the project tracker:
    CERN group:
    IT-CDA
    Status:
    Submitted

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