The distributed NDGF Tier-1 and associated NorduGrid clusters are well integrated into the ATLAS computing environment but follow a slightly different paradigm than other ATLAS resources. The current paradigm does not divide the sites as in the commonly used hierarchical model, but rather treats them as a single storage endpoint and a pool of distributed computing nodes. The next generation ARC middleware with its several new technologies provides new possibilities in development of the ATLAS computing model, such as pilot jobs with pre-cached input files, automatic job migration between the sites, integration of remote sites without connected storage elements, and automatic brokering for jobs with non-standard resource requirements. ARC's data transfer model provides an automatic way for the computing sites to participate in ATLAS' global task management system without requiring centralised brokering or data transfer services. The powerful API combined with Python and Java bindings can easily be used to build new services for job control and data transfer. Integration of the ARC core into the EMI middleware provides a natural way to implement the new services using the ARC components
Staging data to and from remote storage services on the Grid for users' jobs is a vital component of the ARC computing element. A new data staging framework for the computing element has recently been developed to address issues with the present framework, which has essentially remained unchanged since its original implementation 10 years ago. This new framework consists of an intelligent data transfer scheduler which handles priorities and fair-share, a rapid caching system, and the ability to delegate data transfer over multiple nodes to increase network throughput. We use data from real user jobs running on production ARC sites to present an evaluation of the new framework. It is shown to make more efficient use of the available resources, reduce the overall time to run jobs, and avoid the problems seen with the previous simplistic scheduling system. In addition, its simple design coupled with intelligent logic provides greatly increased flexibility for site administrators, end users and future development. Data required by jobs is split into Data Transfer Requests (DTRs) per file.
Abstract. The Advanced Resource Connector (ARC) Grid middleware was designed almost 10 years ago, and has proven to be an attractive distributed computing solution and successful in adapting to new data management and storage technologies. However, with an ever-increasing user base and scale of resources to manage, along with the introduction of more advanced data transfer protocols, some limitations in the current architecture have become apparent. The simple first-in first-out approach to data transfer leads to bottlenecks in the system, as does the built-in assumption that all data is immediately available from remote data storage. We present an entirely new data management architecture for ARC which aims to alleviate these problems, by introducing a three-layer structure. The top layer accepts incoming requests for data transfer and directs them to the middle layer, which schedules individual transfers and negotiates with various intermediate catalog and storage systems until the physical file is ready to be transferred. The lower layer performs all operations which use large amounts of bandwidth, i.e. the physical data transfer. Using such a layered structure allows more efficient use of the available bandwidth as well as enabling late-binding of jobs to data transfer slots based on a priority system. Here we describe in full detail the design and implementation of the new system.
The results of the development of an interactive museum application using augmented reality technology based ontools from the Vuforia environment and the Unity graphic core are presented. The application makes it possible to analyzeand recognize real objects (for example, museum exhibits) that were previously added to Vuforia services for registeringimages. The application has been tested for different mobile devices on iOS and Android platforms. The prospects forusing the proposed application are determined.
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