CAMPUS GRIDS: A FRAMEWORK TO FACILITATE RESOURCE SHARING

It is common at research institutions to maintain multiple clusters. These might fulfill different needs and policies, or represent different owners or generations of hard- ware. Many of these clusters are under utilized while researchers at other departments may require these resources. This may be solved by linking clusters with grid mid- dleware. This thesis describes a distributed high throughput computing framework to link clusters without changing security or execution environments. The framework initially keeps jobs local to the submitter, overflowing if necessary to the campus, and regional grid. The framework is implemented spanning two campuses at the Holland Computing Center. We evaluate the framework for five characteristics of campus grids. This framework is then further expanded to bridge campus grids into a regional grid, and overflow to national cyberinfrastructure

Discovering Job Preemptions in the Open Science Grid

The Open Science Grid(OSG) is a world-wide computing system which facilitates distributed computing for scientific research. It can distribute a computationally intensive job to geo-distributed clusters and process job's tasks in parallel. For compute clusters on the OSG, physical resources may be shared between OSG and cluster's local user-submitted jobs, with local jobs preempting OSG-based ones. As a result, job preemptions occur frequently in OSG, sometimes significantly delaying job completion time. We have collected job data from OSG over a period of more than 80 days. We present an analysis of the data, characterizing the preemption patterns and different types of jobs. Based on observations, we have grouped OSG jobs into 5 categories and analyze the runtime statistics for each category. we further choose different statistical distributions to estimate probability density function of job runtime for different classes.Comment: 8 page

Data Access for LIGO on the OSG

During 2015 and 2016, the Laser Interferometer Gravitational-Wave Observatory (LIGO) conducted a three-month observing campaign. These observations delivered the first direct detection of gravitational waves from binary black hole mergers. To search for these signals, the LIGO Scientific Collaboration uses the PyCBC search pipeline. To deliver science results in a timely manner, LIGO collaborated with the Open Science Grid (OSG) to distribute the required computation across a series of dedicated, opportunistic, and allocated resources. To deliver the petabytes necessary for such a large-scale computation, our team deployed a distributed data access infrastructure based on the XRootD server suite and the CernVM File System (CVMFS). This data access strategy grew from simply accessing remote storage to a POSIX-based interface underpinned by distributed, secure caches across the OSG.Comment: 6 pages, 3 figures, submitted to PEARC1

SciTokens: Capability-Based Secure Access to Remote Scientific Data

The management of security credentials (e.g., passwords, secret keys) for computational science workflows is a burden for scientists and information security officers. Problems with credentials (e.g., expiration, privilege mismatch) cause workflows to fail to fetch needed input data or store valuable scientific results, distracting scientists from their research by requiring them to diagnose the problems, re-run their computations, and wait longer for their results. In this paper, we introduce SciTokens, open source software to help scientists manage their security credentials more reliably and securely. We describe the SciTokens system architecture, design, and implementation addressing use cases from the Laser Interferometer Gravitational-Wave Observatory (LIGO) Scientific Collaboration and the Large Synoptic Survey Telescope (LSST) projects. We also present our integration with widely-used software that supports distributed scientific computing, including HTCondor, CVMFS, and XrootD. SciTokens uses IETF-standard OAuth tokens for capability-based secure access to remote scientific data. The access tokens convey the specific authorizations needed by the workflows, rather than general-purpose authentication impersonation credentials, to address the risks of scientific workflows running on distributed infrastructure including NSF resources (e.g., LIGO Data Grid, Open Science Grid, XSEDE) and public clouds (e.g., Amazon Web Services, Google Cloud, Microsoft Azure). By improving the interoperability and security of scientific workflows, SciTokens 1) enables use of distributed computing for scientific domains that require greater data protection and 2) enables use of more widely distributed computing resources by reducing the risk of credential abuse on remote systems.Comment: 8 pages, 6 figures, PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

IceCube experience using XRootD-based Origins with GPU workflows in PNRP

The IceCube Neutrino Observatory is a cubic kilometer neutrino telescope located at the geographic South Pole. Understanding detector systematic effects is a continuous process. This requires the Monte Carlo simulation to be updated periodically to quantify potential changes and improvements in science results with more detailed modeling of the systematic effects. IceCube's largest systematic effect comes from the optical properties of the ice the detector is embedded in. Over the last few years there have been considerable improvements in the understanding of the ice, which require a significant processing campaign to update the simulation. IceCube normally stores the results in a central storage system at the University of Wisconsin-Madison, but it ran out of disk space in 2022. The Prototype National Research Platform (PNRP) project thus offered to provide both GPU compute and storage capacity to IceCube in support of this activity. The storage access was provided via XRootD-based OSDF Origins, a first for IceCube computing. We report on the overall experience using PNRP resources, with both successes and pain points.Comment: 7 pages, 3 figures, 1 table, To be published in Proceedings of CHEP2

BoscoR: Extending R from the desktop to the Grid

In this paper, we describe a framework to execute R functions on remote resources from the desktop using Bosco. The R language is attractive to researchers because of its high level programming constructs which lower the barrier of entry for use. As the use of the R programming language in HPC and High Throughput Computing (HTC) has grown, so too has the need for parallel libraries in order to utilize computing resources. Bosco is middleware that uses common protocols to manage job submissions to a variety of remote computational platforms and resources. The researcher is able to control and monitor remote submission from their interactive R IDE, such as RStudio. Bosco is capable of managing many concurrent tasks submitted to remote resources while providing feedback to the interactive R environment. We will also show how this framework can be used to access national infrastructure such as the Open Science Grid. Through interviews with R users, and their feedback after using BoscoR, we learned how R users work and designed BoscoR to fit their needs. We incorporated their feedback to improve BoscoR by adding much needed features, such as remote package management. A key design goal was to have a flat learning curve in using BoscoR for any R user

Creating a content delivery network for general science on the internet backbone using XCaches

A general problem faced by computing on the grid for opportunistic users is that delivering cycles is simpler than delivering data to those cycles. In this project we show how we integrated XRootD caches placed on the internet backbone to implement a content delivery network for general science workflows. We will show that for some workflows on different science domains like high energy physics, gravitational waves, and others the combination of data reuse from the workflows together with the use of caches increases CPU efficiency while decreasing network bandwidth use