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A clear 3D representation is vital to prototyping in design. 3D modeling is a popular approach to design structures in 3D, resulting in accurate models that can be viewed from multiple perspectives. However, 3D modeling is too costly and rigid for designers because it has a steep learning curve. Existing heavy-weight 3D software packages are usually not suitable for ideation. Therefore, we consider using sketches to convey ideas in 3D as a more natural way for prototyping. Even if most sketches are loose and 2D, people can still identify 3D structures from them. However, most sketching tools only support drawing in 2D, motivating us to find a solution to bridging 2D sketches and 3D structures. To fill the gap, we propose a 3D sketching system that facilitates sketch creation in 3D. Users can create a sketch in 3D and view it from multiple perspectives, without having to create multiple 2D sketches. This application also supports reconstructing a point cloud from images taken from existing structures. We designed a novel edge detection algorithm derived from the Hough transform to extract borders as strokes from a point cloud reconstructed from captured images. This includes fitting multiple planes to the point cloud and creating strokes by projecting points to the planes. We also present real-world use cases to evaluate our system. Our application allows designers to prototype more rapidly, opening a gate to more user-friendly ideation processes.

The background of this research is the increasing of criminalization in children, one of which is kidnapping of children, Meanwhile many parents have to work so they don’t have enough time to be always close to their children. The purpose of this research is to develop applications that can help a parents to monitor the movements of their children’s outdoor activities. The application uses a smart watch to get the child’s GPS position and at the same time the device sends the last position of the child to the firebase database server. Parents can monitor the child’s movements through the android smart phone device in realtime. The application design uses several UML (unified modeling Language) diagrams namely use case diagrams, activity diagrams and squence diagrams. This application requires data objects including parent, child, family and child’s position (latitude and longitude). The main use cases in this application are sending position, case view, child location on map and view path of moving each child. The application can run only if there is an internet connection and the child uses smart time.

The current experiments in high energy physics (HEP) have a huge data rate. To convert the measured data, an enormous number of computing resources is needed and will further increase with upgraded and newer experiments. To fulfill the ever-growing demand the allocation of additional, potentially only temporary available non-HEP dedicated resources is important. These so-called opportunistic resources cannot only be used for analyses in general but are also well-suited to cover the typical unpredictable peak demands for computing resources. For both use cases, the temporary availability of the opportunistic resources requires a dynamic allocation, integration, and management, while their heterogeneity requires optimization to maintain high resource utilization by allocating best matching resources. To find the best matching resources which should be allocated is challenging due to the unpredictable submission behavior as well as an ever-changing mixture of workflows with different requirements. Instead of predicting the best matching resource, we base our decisions on the utilization of resources. For this reason, we are developing the resource manager TARDIS (Transparent Adaptive Resource Dynamic Integration System) which manages and dynamically requests or releases resources. The decision of how many resources TARDIS has to request is implemented in COBalD (COBald - The Opportunistic Balancing Daemon) to ensure further allocation of well-used resources while reducing the amount of insufficiently used ones. TARDIS allocates and manages resources from various resource providers such as HPC centers or commercial and public clouds while ensuring a dynamic allocation and efficient utilization of these heterogeneous opportunistic resources. Furthermore, TARDIS integrates the allocated opportunistic resources into one overlay batch system which provides a single point of entry for all users. In order to provide the dedicated HEP software environment, we use virtualization and container technologies. In this contribution, we give an overview of the dynamic integration of opportunistic resources via TARDIS/COBalD in our HEP institute as well as how user analyses benefit from these additional resources.

PC vision has turned out to be universal in our general public, with applications in a few fields. Given a lot of pictures, with its inscription, make a prescient model which produces regular, inventive, and intriguing subtitles for the concealed picture. A speedy look at a picture is adequate for a human to call attention to and portray a monstrous measure of insights regarding the visual scene. To rearrange the current issue of producing inscriptions for pictures by making a model which would give exact subtitles to these pictures which can be additionally utilized in other helpful applications and use cases. Be that as it may, this momentous capacity has ended up being a tricky errand for our visual acknowledgment models. Most of the past research in scene acknowledgment has concentrated on naming pictures with a predetermined arrangement of visual classifications and extraordinary advancement has been accom-plished in these undertakings. For a question picture, the past strategies recover pertinent hopeful normal language states by outwardly contrasting the inquiry picture with database pictures. In any case, while shut vocabularies of visual ideas comprise a helpful demonstrating suspicion, they are boundlessly prohibitive when contrasted with the colossal measure of rich depictions that a human can form. These methodologies forced a breaking point on the assortment of inscriptions produced. The model ought to be exempt of suppositions regarding explicit pre decided formats, standards or classes and rather depend on figuring out how to create sentences from the preparation information. The model proposed utilizes Convolution Neural Networks which help to separate highlights of the picture whose subtitle is to be created and afterward by utilizing a probabilistic methodology and Natural Language Processing Techniques reasonable sentences are framed and inscriptions are produced.

In recent years, the development and standardization of 5G networks have received extensive attention. 3GPP officially confirmed that Service-Based Architecture (SBA) is the unified infrastructure of the 5G core network (5GC). The Service-Based Interface (SBI) is an important part of the SBA for interaction between network function services within the 5GC. The fifth generation mobile network will support a range of use cases and requirements that are completely different from traditional 4G networks, such as providing a high level of authentication mechanisms. This paper is organized as follows: First, we compare the difference between 4G authentication and 5G authentication in three aspects including the 4G architecture, authentication process, and communication protocol stack, then implement the simulation environment, and finally evaluate the performance by comparing the delay.

This paper serves as an introduction to the concept of path planning and navigation. Contents include path planning algorithms and their many applications. Specific applications include navigation systems in autonomous/semi-autonomous systems. Often these autonomous systems rely on several layers of sensor data, however at the root is a search-algorithm-based navigation system. This paper is to serve as an introduction to these algorithms and the use cases where young/new roboticists can develop path finding/path planning applications to fit their educational robotics needs. The A*, (A-Star), search algorithm and its applications will be the primary algorithm used within this paper. This paper is a portion of an ongoing research project for a robotic vehicle navigation system.

This article presents the results of the research project: "Time Management Method for Software Development Projects", where a method was used to calculate the time spent on a software development project from its preparation phase until the delivery of it, applying the best practices of the PMBOK 5th edition, the RUP methodology and the estimation metric per Use Cases, so that companies and people dedicated to this activity, execute the project time estimation and carry out an effective programming of their activities. The proposed method consists of six phases: preparation, initiation, planning, development, testing and delivery, where inputs, activities, tools, roles and output artefacts are defined, allowing time to be calculated in each phase. The validation of the method was carried out through of the expert judgment technique, obtaining a favorable result that shows the degree of acceptance of it. According to the evaluation, the method provides a level of precision in the estimates of time, defines activities that provide a basis for estimating, planning, executing, monitoring and controlling the research project, as well as an appropriate logical sequence of activities.

The ATLAS EventIndex System has amassed a set of key quantities for a large number of ATLAS events into a Hadoop based infrastructure for the purpose of providing the experiment with a number of event-wise services. Collecting this data in one place provides the opportunity to investigate various storage formats and technologies and assess which best serve the various use cases as well as consider what other benefits alternative storage systems provide. In this presentation we describe how the data are imported into an Oracle RDBMS, the services we have built based on this architecture, and our experience with it. We've indexed about 15 billion real data events and about 25 billion simulated events thus far and have designed the system to accommodate future data which has expected rates of 5 and 20 billion events per year for real data and simulation, respectively. We have found this system offers outstanding performance for some fundamental use cases. In addition, profiting from the co-location of this data with other complementary metadata in ATLAS, the system has been easily extended to perform essential assessments of data integrity and completeness and to identify event duplication, including at what step in processing the duplication occurred.

22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP2016, San Francisco, USA, 10 Oct 2016 - 14 Oct 2016; Journal of physics / Conference Series 898, 082020 - (2017). doi:10.1088/1742-6596/898/8/082020, As one of the largest resource centres DESY has to support differing work flows of users from various scientific backgrounds. Users can be for one HEP experiments in WLCG or Belle II as well as local HEP users but also physicists from other fields as photon science or accelerator development. By abandoning specific worker node setups in favour of generic flat nodes with middleware resources provided via CVMFS, we gain flexibility to subsume different use cases in a homogeneous environment.Grid jobs and the local batch system are managed in a HTCondor based setup, accepting pilot, user and containerized jobs. The unified setup allows dynamic re-assignment of resources between the different use cases. Monitoring is implemented on global batch system metrics as well as on a per job level utilizing corresponding cgroup information., Published by IOP Publ., Bristol

ROOT provides an flexible format used throughout the HEP community. The number of use cases - from an archival data format to end-stage analysis - has required a number of tradeoffs to be exposed to the user. For example, a high "compression level" in the traditional DEFLATE algorithm will result in a smaller file (saving disk space) at the cost of slower decompression (costing CPU time when read). At the scale of the LHC experiment, poor design choices can result in terabytes of wasted space or wasted CPU time. We explore and attempt to quantify some of these tradeoffs. Specifically, we explore: the use of alternate compressing algorithms to optimize for read performance; an alternate method of compressing individual events to allow efficient random access; and a new approach to whole-file compression. Quantitative results are given, as well as guidance on how to make compression decisions for different use cases., Comment: Proceedings for 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016)

ATLAS's current software framework, Gaudi/Athena, has been very successful for the experiment in LHC Runs 1 and 2. However, its single threaded design has been recognised for some time to be increasingly problematic as CPUs have increased core counts and decreased available memory per core. Even the multi-process version of Athena, AthenaMP, will not scale to the range of architectures we expect to use beyond Run2. ATLAS examined the requirements on an updated multi-threaded framework and layed out plans for a new framework, including better support for high level trigger (HLT) use cases, in 2014. In this paper we report on our progress in developing the new multi-threaded task parallel extension of Athena, AthenaMT. Implementing AthenaMT has required many significant code changes. Progress has been made in updating key concepts of the framework, to allow the incorporation of different levels of thread safety in algorithmic code (from un-migrated thread-unsafe code, to thread safe copyable code to reentrant code). Substantial advances have also been made in implementing a data flow centric design, which has fundamental implications on the structure of the framework, as well as on the development of the new 'event views' infrastructure. These event views support partial event processing and are an essential component to support the HLT's processing of certain regions of interest and we give results from early tests. A major effort has also been invested to have an early version of AthenaMT that can run simulation on many core architectures, which has augmented the understanding gained from work on earlier demonstrators ATLAS demonstrators. We also discuss progress in planning the migration of the large ATLAS algorithmic code base to AthenaMT for Run3.

Distributed data processing has found its application in many fields of science (High Energy and Nuclear Physics (HENP), astronomy, biology to name only those). We have focused our research on distributed data production, an essential part of computations in HENP. Using our previous experience, we have recently proposed a new scheduling approach for distributed data production which is based on the network flow maximization model. It has a polynomial complexity providing required scalability with respect to the size of computations. Our approach improves the overall data production throughput due to three factors: transfer input files in advance before their processing (allows to decrease I/O latency); balancing of the network traffic (includes splitting the load between several alternative transfer paths); and transfer files sequentially in a coordinated manner (allows to reduce the influence of possible network bottlenecks). In this contribution, we present the results of our new simulations based on the GridSim framework which is one of the commonly used tools in the field of distributed computations. In these simulations we study the behavior of standard scheduling approaches compared to our recently proposed approach in a realistic environment relying on the data from the STAR and ATLAS experiments and considering the influence of the background traffic. The final goal of the research is to integrate the proposed scheduling approach into the real data production framework. In order to achieve this we are constantly moving our simulations towards real use cases, study scalability of the model and the influence of the scheduling parameters on the quality of the solution.

The Visual Physics Analysis (VISPA) software is a toolbox for accessing analysis software via the web. It is based on latest web technologies and provides a powerful extension mechanism that enables to interface a wide range of applications. It especially meets the demands of sophisticated experiment-specific use cases that focus on physics data analyses and typically require a high degree of interactivity. As an example, we developed a data inspector which is capable of browsing interactively through event content of several data formats, e.g., MiniAOD which is utilized by the CMS collaboration. Visual control of a chain of user analysis modules as well as visualization of user specific workflows support users in rather complex analyses at the level of ttH cross section measurements. The VISPA extension mechanism is also used to embed external web-based applications which benefit from dynamic allocation of user-defined computing resources via SSH. For example, by wrapping the JSROOT project, ROOT files located on any remote machine can be inspected directly through a VISPA server instance. We present the techniques of the extension mechanism and corresponding applications.

StoRM is an implementation of the SRM interface version 2.2 used by all Large Hadron Collider (LHC) experiments and non-LHC experiments as SRM endpoint at different Tiers of Worldwide LHC Computing Grid. The complexity of its services and the demand of experiments and users are increasing day by day. The growing needs in terms of service level by the StoRM users communities make it necessary to design and implement a more effective testing procedure to quickly and reliably validate new StoRM candidate releases both in code side (for example via test units, and schema valuator) and in final product software (for example via functionality tests, and stress tests). Testing software service is a very critical quality activity performed in a very ad-hoc informal manner by developers, testers and users of StoRM up to now. In this paper, we describe the certification mechanism used by StoRM team to increase the robustness and reliability of the StoRM services. Various typologies of tests, such as quality, installation, configuration, functionality, stress and performance, defined on the base of a set of use cases gathered as consequence of the collaboration among the StoRM team, experiments and users, are illustrated. Each typology of test is either increased or decreased easily from time to time. The proposed mechanism is based on a new configurable testsuite. This is executed by the certification team, who is responsible for validating the release candidate package as well as bug fix (or patch) package, given a certain testbed that considers all possible use cases. In correspondence of each failure, the package is given back to developers waiting for validating a new package.

The role of cloud computing technology in the distributed computing for HEP experiments grows rapidly. Some experiments (Atlas, BES-III, LHCb) already exploit private and public cloud resources for the data processing. Future experiments such as Belle II or upgraded LHC experiments will largely rely on the availability of cloud resources and therefore their computing models have to be adjusted to the specific features of cloud environment, in particular to the on-demand computing paradigm.Belle II experiment at SuperKEKB will start physics run in 2017. Belle II computing requirements are challenging. The data size at the level of hundred PB is expected after several years of operation, around 2020. The baseline solution selected for distributed processing is the DIRAC system. DIRAC can handle variety of computing resources including Grids, Clouds and independent clusters. Cloud resources can be connected by VMDIRAC module through public interfaces. In particular the mechanism of dynamic activation of new virtual machines with reserved job slots for new tasks in case of an increasing demand for computing resources is introduced.This work is focused on VMDIRAC interaction with public (Amazon EC2) and private (CC1) cloud. The solution applied by Belle II experiment and the experience from Monte Carlo production campaigns will be presented. Updated computation costs for different use cases will be shown.

Geant4 is a simulation system of particle transport through matter, widely used in several experimental areas from high energy physics and nuclear experiments to medical studies. Some of its applications may involve critical use cases; therefore they would benefit from an objective assessment of the software quality of Geant4. In this paper, we provide a first statistical evaluation of software metrics data related to a set of Geant4 physics packages. The analysis aims at identifying risks for Geant4 maintainability, which would benefit from being addressed at an early stage. The findings of this pilot study set the grounds for further extensions of the analysis to the whole of Geant4 and to other high energy physics software systems.

The Geant4 physicsjists package encompasses predefined selections of physics processes and models to be used in simulation applications. Limited documentation is available in the literature about Geant4 pre-packaged PhysicsLists and their validation. The reports in the literature mainly concern specific use cases. This paper documents the epistemological grounds for the validation of Geant4 pre-packaged PhysicsLists (and their accessory classes, Builders and PhysicsConstructors) and some examples of the author's scientific activity on this subject.

The Computing Models of the LHC experiments continue to evolve from the simple hierarchical MONARC[2] model towards more agile models where data is exchanged among many Tier2 and Tier3 sites, relying on both large scale file transfers with strategic data placement, and an increased use of remote access to object collections with caching through CMS's AAA, ATLAS' FAX and ALICE's AliEn projects, for example. The challenges presented by expanding needs for CPU, storage and network capacity as well as rapid handling of large datasets of file and object collections have pointed the way towards future more agile pervasive models that make best use of highly distributed heterogeneous resources. In this paper, we explore the use of Named Data Networking (NDN), a new Internet architecture focusing on content rather than the location of the data collections. As NDN has shown considerable promise in another data intensive field, Climate Science, we discuss the similarities and differences between the Climate and HEP use cases, along with specific issues HEP faces and will face during LHC Run2 and beyond, which NDN could address.

The Worldwide LHC Computing Grid relies on the network as a critical part of its infrastructure and therefore needs to guarantee effective network usage and prompt detection and resolution of any network issues, including connection failures, congestion, traffic routing, etc. The WLCG Network and Transfer Metrics project aims to integrate and combine all network-related monitoring data collected by the WLCG infrastructure. This includes FTS monitoring information, monitoring data from the XRootD federation, as well as results of the perfSONAR tests. The main challenge consists of further integrating and analyzing this information in order to allow the optimizing of data transfers and workload management systems of the LHC experiments. In this contribution, we present our activity in commissioning WLCG perfSONAR network and integrating network and transfer metrics: We motivate the need for the network performance monitoring, describe the main use cases of the LHC experiments as well as status and evolution in the areas of configuration and capacity management, datastore and analytics, including integration of transfer and network metrics and operations and support.

Tier-1 sites providing computing power for HEP experiments are usually tightly designed for high throughput performances. This is pursued by reducing the variety of supported use cases and tuning for performances those ones, the most important of which have been that of singlecore jobs. Moreover, the usual workload is saturation: each available core in the farm is in use and there are queued jobs waiting for their turn to run. Enabling multi-core jobs thus requires dedicating a number of hosts where to run, and waiting for them to free the needed number of cores. This drain-time introduces a loss of computing power driven by the number of unusable empty cores. As an increasing demand for multi-core capable resources have emerged, a Task Force have been constituted in WLCG, with the goal to define a simple and efficient multi-core resource provisioning model. This paper details the work done at the INFN Tier-1 to enable multi-core support for the LSF batch system, with the intent of reducing to the minimum the average number of unused cores. The adopted strategy has been that of dedicating to multi-core a dynamic set of nodes, whose dimension is mainly driven by the number of pending multi-core requests and fair-share priority of the submitting user. The node status transition, from single to multi core et vice versa, is driven by a finite state machine which is implemented in a custom multi-core director script, running in the cluster. After describing and motivating both the implementation and the details specific to the LSF batch system, results about performance are reported. Factors having positive and negative impact on the overall efficiency are discussed and solutions to reduce at most the negative ones are proposed.

In preparation for the new era of RHIC running (RHIC-II upgrades and possibly, the eRHIC era), the STAR experiment is expanding its modular Message Interface and Reliable Architecture framework (MIRA). MIRA allowed STAR to integrate meta-data collection, monitoring, and online QA components in a very agile and efficient manner using a messaging infrastructure approach. In this paper, we briefly summarize our past achievements, provide an overview of the recent development activities focused on messaging patterns and describe our experience with the complex event processor (CEP) recently integrated into the MIRA framework. CEP was used in the recent RHIC Run 14, which provided practical use cases. Finally, we present our requirements and expectations for the planned expansion of our systems, which will allow our framework to acquire features typically associated with Detector Control Systems. Special attention is given to aspects related to latency, scalability and interoperability within heterogeneous set of services, various data and meta-data acquisition components coexisting in STAR online domain.

Modern software applications rarely live in isolation and nowadays it is common practice to rely on services or consume information provided by remote entities. In such a distributed architecture, integration is key. Messaging, for more than a decade, is the reference solution to tackle challenges of a distributed nature, such as network unreliability, strong-coupling of producers and consumers and the heterogeneity of applications. Thanks to a strong community and a common effort towards standards and consolidation, message brokers are today the transport layer building blocks in many projects and services, both within the physics community and outside. Moreover, in recent years, a new generation of messaging services has appeared, with a focus on low-latency and high-performance use cases, pushing the boundaries of messaging applications. This paper will present messaging solutions for distributed applications going through an overview of the main concepts, technologies and services.

ALICE (A Large Ion Collider Experiment) is a detector designed to study the physics of strongly interacting matter produced in heavy-ion collisions at the CERN Large Hadron Collider (LHC). Due to the complexity of ALICE in terms of number of detectors and performance requirements, Data Quality Monitoring (DQM) plays an essential role in providing online feedback on the data being recorded. It intends to provide shifters with precise and complete information to quickly identify problems, and as a consequence to ensure acquisition of high quality data. This paper presents a review of the ALICE DQM system during the first three years of LHC operations from a quantitative and qualitative point of view. We start by presenting the DQM software and tools before moving on to the various analyses carried out. An overview of the produced monitoring quantities is given, presenting the diversity of usage and flexibility of the DQM. Well-prepared shifters and experts, in addition to a precise organisation, were required to ensure smooth and successful operations. The description of the measures taken to ensure both aspects and an account of the DQM shifters' job are followed by a summary of the evolution of the system. We then give a quantitative review of the final setup of the system used during the whole year 2012. We conclude the paper with use cases where the DQM proved to be very valuable, scalable and efficient and with the plans for the coming years.

ATLAS, CERN-IT, and CMS embarked on a project to develop a common system for analysis workflow management, resource provisioning and job scheduling. This distributed computing infrastructure was based on elements of PanDA and prior CMS workflow tools. After an extensive feasibility study and development of a proof-of-concept prototype, the project now has a basic infrastructure that supports the analysis use cases of both experiments via common services. In this paper we will discuss the state of the current solution and give an overview of all the components of the system.

In a typical scientific computing centre, diverse applications coexist and share a single physical infrastructure. An underlying Private Cloud facility eases the management and maintenance of heterogeneous use cases such as multipurpose or application-specific batch farms, Grid sites catering to different communities, parallel interactive data analysis facilities and others. It allows to dynamically and efficiently allocate resources to any application and to tailor the virtual machines according to the applications' requirements. Furthermore, the maintenance of large deployments of complex and rapidly evolving middleware and application software is eased by the use of virtual images and contextualization techniques; for example, rolling updates can be performed easily and minimizing the downtime. In this contribution we describe the Private Cloud infrastructure at the INFN-Torino Computer Centre, that hosts a full-fledged WLCG Tier-2 site and a dynamically expandable PROOF-based Interactive Analysis Facility for the ALICE experiment at the CERN LHC and several smaller scientific computing applications. The Private Cloud building blocks include the OpenNebula software stack, the GlusterFS filesystem (used in two different configurations for worker- and service-class hypervisors) and the OpenWRT Linux distribution (used for network virtualization). A future integration into a federated higher-level infrastructure is made possible by exposing commonly used APIs like EC2 and by using mainstream contextualization tools like CloudInit.

The WLCG infrastructure moved from a very rigid network topology, based on the MONARC model, to a more relaxed system, where data movement between regions or countries does not necessarily need to involve T1 centres. While this evolution brought obvious advantages, especially in terms of flexibility for the LHC experiment's data management systems, it also opened the question of how to monitor the increasing number of possible network paths, in order to provide a global reliable network service. The perfSONAR network monitoring system has been evaluated and agreed as a proper solution to cover the WLCG network monitoring use cases: it allows WLCG to plan and execute latency and bandwidth tests between any instrumented endpoint through a central scheduling configuration, it allows archiving of the metrics in a local database, it provides a programmatic and a web based interface exposing the tests results; it also provides a graphical interface for remote management operations. In this contribution we will present our activity to deploy a perfSONAR based network monitoring infrastructure, in the scope of the WLCG Operations Coordination initiative: we will motivate the main choices we agreed in terms of configuration and management, describe the additional tools we developed to complement the standard packages and present the status of the deployment, together with the possible future evolution.

Due to conceptual difference between geometry descriptions in Computer-Aided Design (CAD) systems and particle transport Monte Carlo (MC) codes direct conversion of detector geometry in either direction is not feasible. The paper presents an update on functionality and application practice of the CATIA-GDML geometry builder first introduced at CHEP2010. This set of CATIAv5 tools has been developed for building a MC optimized GEANT4/ROOT compatible geometry based on the existing CAD model. The model can be exported via Geometry Description Markup Language (GDML). The builder allows also import and visualization of GEANT4/ROOT geometries in CATIA. The structure of a GDML file, including replicated volumes, volume assemblies and variables, is mapped into a part specification tree. A dedicated file template, a wide range of primitives, tools for measurement and implicit calculation of parameters, different types of multiple volume instantiation, mirroring, positioning and quality check have been implemented. Several use cases are discussed.

Visual Physics Analysis (VISPA) is a web-based development environment addressing high energy and astroparticle physics. It covers the entire analysis spectrum from the design and validation phase to the execution of analyses and the visualization of results. VISPA provides a graphical steering of the analysis flow, which consists of self-written, re-usable Python and C++ modules for more demanding tasks. All common operating systems are supported since a standard internet browser is the only software requirement for users. Even access via mobile and touch-compatible devices is possible. In this contribution, we present the most recent developments of our web application concerning technical, state-of-the-art approaches as well as practical experiences. One of the key features is the use of workspaces, i.e. user-configurable connections to remote machines supplying resources and local file access. Thereby, workspaces enable the management of data, computing resources (e.g. remote clusters or computing grids), and additional software either centralized or individually. We further report on the results of an application with more than 100 third-year students using VISPA for their regular particle physics exercises during the winter term 2012/13. Besides the ambition to support and simplify the development cycle of physics analyses, new use cases such as fast, location-independent status queries, the validation of results, and the ability to share analyses within worldwide collaborations with a single click become conceivable.

The analysis and interpretation of data collected by the Large Hadron Collider (LHC) requires advanced statistical tools in order to quantify the agreement between observation and theoretical models. RooStats is a project providing a statistical framework for data analysis with the focus on discoveries, confidence intervals and combination of different measurements in both Bayesian and frequentist approaches. It employs the RooFit data modelling language where mathematical concepts such as variables, (probability density) functions and integrals are represented as C++ objects. RooStats and RooFit rely on the persistency technology of the ROOT framework. The usage of a common data format enables the concept of digital publishing of complicated likelihood functions. The statistical tools have been developed in close collaboration with the LHC experiments to ensure their applicability to real-life use cases. Numerous physics results have been produced using the RooStats tools, with the discovery of the Higgs boson by the ATLAS and CMS experiments being certainly the most popular among them. We will discuss tools currently used by LHC experiments to set exclusion limits, to derive confidence intervals and to estimate discovery significances based on frequentist statistics and the asymptotic behaviour of likelihood functions. Furthermore, new developments in RooStats and performance optimisation necessary to cope with complex models depending on more than 1000 variables will be reviewed.

The PanDA (Production and Distributed Analysis) system has been developed to meet ATLAS production and analysis requirements for a data-driven workload management system capable of operating at LHC data processing scale. PanDA has performed well with high reliability and robustness during the two years of LHC data-taking, while being actively evolved to meet the rapidly changing requirements for analysis use cases. We will present an overview of system evolution including automatic rebrokerage and reattempt for analysis jobs, adaptation for the CernVM File System, support for the multi-cloud model through which Tier-2 sites act as members of multiple clouds, pledged resource management and preferential brokerage, and monitoring improvements. We will also describe results from the analysis of two years of PanDA usage statistics, current issues, and plans for the future.

The CORAL software is widely used by the LHC experiments for storing and accessing data using relational database technologies. CORAL provides a C++ abstraction layer that supports data persistency for several back-ends and deployment models, direct client access to Oracle servers being one of the most important use cases. Since 2010, several problems have been reported by the LHC experiments in their use of Oracle through CORAL, involving application errors, hangs or crashes after the network or the database servers became temporarily unavailable. CORAL already provided some level of handling of these instabilities, which are due to external causes and cannot be avoided, but this proved to be insufficient in some cases and to be itself the cause of other problems, such as the hangs and crashes mentioned before, in other cases. As a consequence, a major redesign of the CORAL plugins has been implemented, with the aim of making the software more robust against these database and network glitches. The new implementation ensures that CORAL automatically reconnects to Oracle databases in a transparent way whenever possible and gently terminates the application when this is not possible. Internally, this is done by resetting all relevant parameters of the underlying back-end technology (OCI, the Oracle Call Interface). This presentation reports on the status of this work at the time of the CHEP2012 conference, covering the design and implementation of these new features and the outlook for future developments in this area.

The Open Science Grid (OSG) supports a diverse community of new and existing users in adopting and making effective use of the Distributed High Throughput Computing (DHTC) model. The LHC user community has deep local support within the experiments. For other smaller communities and individual users the OSG provides consulting and technical services through the User Support area. We describe these sometimes successful and sometimes not so successful experiences and analyze lessons learned that are helping us improve our services. The services offered include forums to enable shared learning and mutual support, tutorials and documentation for new technology, and troubleshooting of problematic or systemic failure modes. For new communities and users, we bootstrap their use of the distributed high throughput computing technologies and resources available on the OSG by following a phased approach. We first adapt the application and run a small production campaign on a subset of "friendly" sites. Only then do we move the user to run full production campaigns across the many remote sites on the OSG, adding to the community resources up to hundreds of thousands of CPU hours per day. This scaling up generates new challenges – like no determinism in the time to job completion, and diverse errors due to the heterogeneity of the configurations and environments – so some attention is needed to get good results. We cover recent experiences with image simulation for the Large Synoptic Survey Telescope (LSST), small-file large volume data movement for the Dark Energy Survey (DES), civil engineering simulation with the Network for Earthquake Engineering Simulation (NEES), and accelerator modeling with the Electron Ion Collider group at BNL. We will categorize and analyze the use cases and describe how our processes are evolving based on lessons learned.

In recent years, several new storage technologies, such as Lustre, Hadoop, OrangeFS, and BlueArc, have emerged. While several groups have run benchmarks to characterize them under a variety of configurations, more work is needed to evaluate these technologies for the use cases of scientific computing on Grid clusters and Cloud facilities. This paper discusses our evaluation of the technologies as deployed on a test bed at FermiCloud, one of the Fermilab infrastructure-as-a-service Cloud facilities. The test bed consists of 4 server-class nodes with 40 TB of disk space and up to 50 virtual machine clients, some running on the storage server nodes themselves. With this configuration, the evaluation compares the performance of some of these technologies when deployed on virtual machines and on "bare metal" nodes. In addition to running standard benchmarks such as IOZone to check the sanity of our installation, we have run I/O intensive tests using physics-analysis applications. This paper presents how the storage solutions perform in a variety of realistic use cases of scientific computing. One interesting difference among the storage systems tested is found in a decrease in total read throughput with increasing number of client processes, which occurs in some implementations but not others.

The Conditions Database (CondDB) of the LHCb experiment provides versioned, time dependent geometry and conditions data for all LHCb data processing applications (simulation, high level trigger (HLT), reconstruction, analysis) in a heterogeneous computing environment ranging from user laptops to the HLT farm and the Grid. These different use cases impose front-end support for multiple database technologies (Oracle and SQLite are used). Sophisticated distribution tools are required to ensure timely and robust delivery of updates to all environments. The content of the database has to be managed to ensure that updates are internally consistent and externally compatible with multiple versions of the physics application software. In this paper we describe three systems that we have developed to address these issues. The first system is a CondDB state tracking extension to the Oracle 3D Streams replication technology, to trap cases when the CondDB replication was corrupted. Second, an automated distribution system for the SQLite-based CondDB, providing also smart backup and checkout mechanisms for the CondDB managers and LHCb users respectively. And, finally, a system to verify and monitor the internal (CondDB self-consistency) and external (LHCb physics software vs. CondDB) compatibility. The former two systems are used in production in the LHCb experiment and have achieved the desired goal of higher flexibility and robustness for the management and operation of the CondDB. The latter one has been fully designed and is passing currently to the implementation stage.

This paper describes the investigative study undertaken to evaluate shared filesystem performance and suitability in the LHCb Online environment. Particular focus is given to the measurements and field tests designed and performed on an in-house OpenAFS setup; related comparisons with NFSv4 and GPFS (a clustered filesystem from IBM) are presented. The motivation for the investigation and the test setup arises from the need to serve common user-space like home directories, experiment software and control areas, and clustered log areas. Since the operational requirements on such user-space are stringent in terms of read-write operations (in frequency and access speed) and unobtrusive data relocation, test results are presented with emphasis on file-level performance, stability and "high-availability" of the shared filesystems. Use cases specific to the experiment operation in LHCb, including the specific handling of shared filesystems served to a cluster of 1500 diskless nodes, are described. Issues of prematurely expiring authenticated sessions are explicitly addressed, keeping in mind long-running analysis jobs on the Online cluster. In addition, quantitative test results are also presented with alternatives including NFSv4. Comparative measurements of filesystem performance benchmarks are presented, which are seen to be used as reference for decisions on potential migration of the current storage solution deployed in the LHCb online cluster.

Detector simulation is one of the most CPU intensive tasks in modern High Energy Physics. While its importance for the design of the detector and the estimation of the efficiency is ever increasing, the amount of events that can be simulated is often constrained by the available computing resources. Various kind of "fast simulations" have been developed to alleviate this problem, however, while successful, these are mostly "ad hoc" solutions which do not replace completely the need for detailed simulations. One of the common features of both detailed and fast simulation is the inability of the codes to exploit fully the parallelism which is increasingly offered by the new generations of CPUs. In the next years it is reasonable to expect an increase on one side of the needs for detector simulation, and on the other in the parallelism of the hardware, widening the gap between the needs and the available means. In the past years, and indeed since the beginning of simulation programs, several unsuccessful efforts have been made to exploit the "embarrassing parallelism" of simulation programmes. After a careful study of the problem, and based on a long experience in simulation codes, the authors have concluded that an entirely new approach has to be adopted to exploit parallelism. The paper will review the current prototyping work, encompassing both detailed and fast simulation use cases. Performance studies will be presented, together with a roadmap to develop a new full-fledged transport program efficiently exploiting parallelism for the physics and geometry computations, while adapting the steering mechanisms to accommodate detailed and fast simulation in a single framework.

While the LHC data movement systems have demonstrated the ability to move data at the necessary throughput, we have identified two weaknesses: the latency for physicists to access data and the complexity of the tools involved. To address these, both ATLAS and CMS have begun to federate regional storage systems using Xrootd. Xrootd, referring to a protocol and implementation, allows us to provide data access to all disk-resident data from a single virtual endpoint. This "redirector" discovers the actual location of the data and redirects the client to the appropriate site. The approach is particularly advantageous since typically the redirection requires much less than 500 milliseconds and the Xrootd client is conveniently built into LHC physicists' analysis tools. Currently, there are three regional storage federations - a US ATLAS region, a European CMS region, and a US CMS region. The US ATLAS and US CMS regions include their respective Tier 1, Tier 2 and some Tier 3 facilities; a large percentage of experimental data is available via the federation. Additionally, US ATLAS has begun studying low-latency regional federations of close-by sites. From the base idea of federating storage behind an endpoint, the implementations and use cases diverge. The CMS software framework is capable of efficiently processing data over high-latency links, so using the remote site directly is comparable to accessing local data. The ATLAS processing model allows a broad spectrum of user applications with varying degrees of performance with regard to latency; a particular focus has been optimizing n-tuple analysis. Both VOs use GSI security. ATLAS has developed a mapping of VOMS roles to specific file system authorizations, while CMS has developed callouts to the site's mapping service. Each federation presents a global namespace to users. For ATLAS, the global-to-local mapping is based on a heuristic-based lookup from the site's local file catalog, while CMS does the mapping based on translations given in a configuration file. We will also cover the latest usage statistics and interesting use cases that have developed over the previous 18 months.

The ATLAS experiment at LHC relies on databases for detector online data-taking, storage and retrieval of configurations, calibrations and alignments, post data-taking analysis, file management over the grid, job submission and management, condition data replication to remote sites. Oracle Relational Database Management System (RDBMS) has been addressing the ATLAS database requirements to a great extent for many years. Ten database clusters are currently deployed for the needs of the different applications, divided in production, integration and standby databases. The data volume, complexity and demands from the users are increasing steadily with time. Nowadays more than 20 TB of data are stored in the ATLAS production Oracle databases at CERN (not including the index overhead), but the most impressive number is the hosted 260 database schemas (for the most common case each schema is related to a dedicated client application with its own requirements). At the beginning of 2012 all ATLAS databases at CERN have been upgraded to the newest Oracle version at the time: Oracle 11g Release 2. Oracle 11g comes with several key improvements compared to previous database engine versions. In this work we present our evaluation of the most relevant new features of 11g of interest for ATLAS applications and use cases. Notably we report on the performance and scalability enhancements obtained in production since the deployment 11g new features during Q1 2012 and we outline plans for future work in this area.

The LHCb experiment has been using the CMT build and configuration tool for its software since the first versions, mainly because of its multi-platform build support and its powerful configuration management functionality. Still, CMT has some limitations in terms of build performance and the increased complexity added to the tool to cope with new use cases added recently. Therefore, we have been looking for a viable alternative and we have investigated the possibility of adopting the CMake tool, which does a very good job for building and is getting very popular in the HEP community. The result of this study is a CMake-based framework which provides most of the special configuration features available natively only in CMT, with the advantages of better performances, flexibility and portability.

The PROOF benchmark suite is a new utility suite of PROOF to measure performance and scalability. The primary goal of the benchmark suite is to determine optimal configuration parameters for a set of machines to be used as PROOF cluster. The suite measures the performance of the cluster for a set of standard tasks as a function of the number of effective processes. Cluster administrators can use the suite to measure the performance of the cluster and find optimal configuration parameters. PROOF developers can also utilize the suite to help them measure, identify problems and improve their software. In this paper, the new tool is explained in detail and use cases are presented to illustrate the new tool.

Traditional relational databases have not always been well matched to the needs of data-intensive sciences, but efforts are underway within the database community to attempt to address many of the requirements of large-scale scientific data management. One such effort is the open-source project SciDB. Since its earliest incarnations, SciDB has been designed for scalability in parallel and distributed environments, with a particular emphasis upon native support for array constructs and operations. Such scalability is of course a requirement of any strategy for large-scale scientific data handling, and array constructs are certainly useful in many contexts, but these features alone do not suffice to qualify a database product as an appropriate technology for hosting particle physics or cosmology data. In what constitutes its 1.0 release in June 2011, SciDB has extended its feature set to address additional requirements of scientific data, with support for user-defined types and functions, for data versioning, and more. This paper describes an evaluation of the capabilities of SciDB for two very different kinds of physics data: event-level metadata records from proton collisions at the Large Hadron Collider (LHC), and the output of cosmological simulations run on very-large-scale supercomputers. This evaluation exercises the spectrum of SciDB capabilities in a suite of tests that aim to be representative and realistic, including, for example, definition of four-vector data types and natural operations thereon, and computational queries that match the natural use cases for these data.

Visual Physics Analysis (VISPA) is an analysis environment with applications in high energy and astroparticle physics. Based on a data-flow-driven paradigm, it allows users to combine graphical steering with self-written C++ and Python modules. This contribution presents new concepts integrated in VISPA: layers, convenient analysis execution, and web-based physics analysis. While the convenient execution offers full flexibility to vary settings for the execution phase of an analysis, layers allow to create different views of the analysis already during its design phase. Thus, one application of layers is to define different stages of an analysis (e.g. event selection and statistical analysis). However, there are other use cases such as to independently optimize settings for different types of input data in order to guide all data through the same analysis flow. The new execution feature makes job submission to local clusters as well as the LHC Computing Grid possible directly from VISPA. Web-based physics analysis is realized in the VISPA@Web project, which represents a whole new way to design and execute analyses via a standard web browser.

Predictive maintenance offers the possibility to prognosticate the remaining time until a maintenance action of a machine has to be scheduled. Unfortunately, current predictive maintenance solutions are only suitable for very specific use cases like reliability predictions based on vibration monitoring. Furthermore, they do not consider the fact that machines may deteriorate non-uniformly, depending on external influences (e.g., the work piece material in a milling machine or the changing fruit acid concentration in a bottling plant). In this paper two concepts for a generic predictive maintenance solution which also considers non-uniformly aging behaviour are introduced. The first concept is based on system models representing the health state of a technical system. As these models are usually statically (viz. without a timely dimension) their coefficients are determined periodically and the resulting time series is used as aging indicator. The second concept focuses on external influences (contexts) which change the behaviour of the previous mentioned aging indicators in order to increase the accuracy of reliability predictions. Therefore, context-depended time series models are determined and used to predict machine reliability. Both concepts were evaluated on data of an air ventilation system. Thereby, it could be shown that they are suitable to determine aging indicators in a generic way and to incorporate external influences in the reliability prediction. Through this, the quality of reliability predictions can be significantly increased. In reality this leads to a more accurate scheduling of maintenance actions. Furthermore, the generic character of the solutions makes the concepts suitable for a wide range of aging processes.

The Tier-0 processing system is the initial stage of the multi-tiered computing system of CMS. It takes care of the first processing steps of data at the LHC at CERN. The automated workflows running in the Tier-0 contain both low-latency processing chains for time-critical applications and bulk chains to archive the recorded data offsite the host laboratory. It is a mix between an online and offline system, because the data the CMS DAQ writes out initially is of a temporary nature. Most of the complexity in the design of this system comes from this unique combination of online and offline use cases and dependencies. In this talk, we want to present the software design of the CMS Tier-0 system and present an analysis of the 24/7 operation of the system in the 2009/2010 data taking periods.

There are various use cases where one has to separate user environments from the provided machine hardware, operating system or software setup on a cluster. To achieve this, a lightweight implementation of dynamically virtualized worker nodes in common batch systems is presented. Additionally, a summary of experiences and performance measurements obtained by running such a virtualized cluster, shared between nine different departments of the Karlsruhe Institute of Technology, is presented.

Our site successfully runs more than a Petabyte of online disk, using nothing but Direct Attached Storage. The bulk of this capacity is grid-enabled and served by dCache, but sizable amounts are provided by traditional AFS or modern Lustre filesystems as well. While each of these storage flavors has a different purpose, owing to their respective strengths and weaknesses for certain use cases, their instances are all built from the same universal storage bricks. These are managed using the same scale-out techniques used for compute nodes, and run the same operating system as those, thus fully leveraging the existing know-how and infrastructure. As a result, this storage is cost effective especially regarding total cost of ownership. It is also competitive in terms of aggregate performance, performance per capacity, and – due to the possibility to make use of the latest technology early – density and power efficiency. Further advantages include a high degree of flexibility and complete avoidance of vendor lock-in. Availability and reliability in practice turn out to be more than adequate for a HENP site's major tasks. We present details about this Ansatz for online storage, hardware and software used, tweaking and tuning, lessons learned, and the actual result in practice.

The project VISPA@WEB provides a novel graphical development environment for physics analyses which only requires a standard web browser on the client machine. It resembles the existing analysis environment available from the project Visual Physics Analysis VISPA, including the connection and configuration of modules for different tasks. High level logic can be programmed using the Python language, while performance-critical tasks can be implemented in C++ modules. The use cases range from simple teaching examples to highly complex scientific analyses.

The increases of data size and in the geographical distribution of analysis intensify the demand on the I/O subsystem. Over the last year, we greatly improved the I/O throughput, in some case by several factors, when reading ROOT files. ROOT'S improved techniques include improving the pre-existing prefetching, the automatic flushing of data buffers at regular intervals and streaming objects member-wise. These advances reduce the number of transactions with the local disk or the network. We worked in close collaboration with the Large Hadron Collider (LHC) experiments to optimize the I/O access to their use cases and to help adapt their framework to take full advantage of these advances. This presentation will describe in details these improvements and how users can benefit from them.

Novosibirsk Scientific Center (NSC), also known worldwide as Akademgorodok, is one of the largest Russian scientific centers hosting Novosibirsk State University (NSU) and more than 35 research organizations of the Siberian Branch of Russian Academy of Sciences including Budker Institute of Nuclear Physics (BINP), Institute of Computational Technologies (ICT), and Institute of Computational Mathematics and Mathematical Geophysics (ICM&MG). Since each institute has specific requirements on the architecture of the computing farms involved in its research field, currentiy we've got several computing facilities hosted by NSC institutes, each optimized for the particular set of tasks, of which the largest are the NSU Supercomputer Center, Siberian Supercomputer Center (ICM&MG), and a Grid Computing Facility of BINP. Recendy a dedicated optical network with the initial bandwidth of 10 Gbps connecting these three facilities was built in order to make it possible to share the computing resources among the research communities of participating institutes, thus providing a common platform for building the computing infrastructure for various scientific projects. Unification of the computing infrastructure is achieved by extensive use of virtualization technologies based on XEN and KVM platforms. The solution implemented was tested thoroughly within the computing environment of KEDR detector experiment which is being carried out at BINP, and foreseen to be applied to the use cases of other HEP experiments in the upcoming future.

During the EGEE-III project operational tools such as SAM, Nagios, Gridview, the regional Dashboard and GGUS moved to a communication architecture based on ActiveMQ, an open-source enterprise messaging solution. LHC experiments, in particular ATLAS, developed prototypes of systems using the same messaging infrastructure, validating the system for their use-cases. In this paper we describe the WLCG messaging use cases and outline an improved messaging architecture based on the experience gained during the EGEE-III period. We show how this provides a solid basis for many applications, including the grid middleware, to improve their resilience and reliability.