arXiv subjects

Derek Weitzel

Publications and source records attributed to Derek Weitzel.

18 recordsLinked to original sources

arXiv2026

Open Science Data Federation -- operation and monitoring

Extensive data processing is becoming commonplace in many fields of science. Distributing data to processing sites and providing methods to share the data with collaborators efficiently has become essential. The Open Science Data Federation (OSDF) builds upon the successful StashCache project to create a global data access network. The OSDF expands the StashCache project to add new data origins and caches, access methods, monitoring, and accounting mechanisms. Additionally, the OSDF has become an integral part of the U.S. national cyberinfrastructure landscape due to the sharing requirements of recent NSF solicitations, which the OSDF is uniquely positioned to enable. The OSDF continues to be utilized by many research collaborations and individual users, which pull the data to many research infrastructures and projects.

cs.DC↗

arXiv2025

The National Research Platform: Stretched, Multi-Tenant, Scientific Kubernetes Cluster

The National Research Platform (NRP) represents a distributed, multi-tenant Kubernetes-based cyberinfrastructure designed to facilitate collaborative scientific computing. Spanning over 75 locations in the U.S. and internationally, the NRP uniquely integrates varied computational resources, ranging from single nodes to extensive GPU and CPU clusters, to support diverse research workloads including advanced AI and machine learning tasks. It emphasizes flexibility through user-friendly interfaces such as JupyterHub and low level control of resources through direct Kubernetes interaction. Critical operational insights are discussed, including security enhancements using Kubernetes-integrated threat detection, extensive monitoring, and comprehensive accounting systems. This paper highlights the NRP's growing importance and scalability in addressing the increasing demands for distributed scientific computational resources.

cs.DC↗

arXiv2025

The 200 Gbps Challenge: Imagining HL-LHC analysis facilities

The IRIS-HEP software institute, as a contributor to the broader HEP Python ecosystem, is developing scalable analysis infrastructure and software tools to address the upcoming HL-LHC computing challenges with new approaches and paradigms, driven by our vision of what HL-LHC analysis will require. The institute uses a "Grand Challenge" format, constructing a series of increasingly large, complex, and realistic exercises to show the vision of HL-LHC analysis. Recently, the focus has been demonstrating the IRIS-HEP analysis infrastructure at scale and evaluating technology readiness for production. As a part of the Analysis Grand Challenge activities, the institute executed a "200 Gbps Challenge", aiming to show sustained data rates into the event processing of multiple analysis pipelines. The challenge integrated teams internal and external to the institute, including operations and facilities, analysis software tools, innovative data delivery and management services, and scalable analysis infrastructure. The challenge showcases the prototypes - including software, services, and facilities - built to process around 200 TB of data in both the CMS NanoAOD and ATLAS PHYSLITE data formats with test pipelines. The teams were able to sustain the 200 Gbps target across multiple pipelines. The pipelines focusing on event rate were able to process at over 30 MHz. These target rates are demanding; the activity revealed considerations for future testing at this scale and changes necessary for physicists to work at this scale in the future. The 200 Gbps Challenge has established a baseline on today's facilities, setting the stage for the next exercise at twice the scale.

hep-ex↗

arXiv2025

Tuning the CMS Coffea-casa facility for 200 Gbps Challenge

As a part of the IRIS-HEP "Analysis Grand Challenge" activities, the Coffea-casa AF team executed a "200 Gbps Challenge". One of the goals of this challenge was to provide a setup for execution of a test notebook-style analysis on the facility that could process a 200 TB CMS NanoAOD dataset in 20 minutes. We describe the solutions we deployed at the facility to execute the challenge tasks. The facility was configured to provide 2000+ cores for quick turn-around, low-latency analysis. To reach the highest event processing rates we tested different scaling backends, both scaling over HTCondor and Kubernetes resources and using Dask and Taskvine schedulers. This configuration also allowed us to compare two different services for managing Dask clusters, Dask labextention, and Dask Gateway server, under extreme conditions. A robust set of XCache servers with a redirector were deployed in Kubernetes to cache the dataset to minimize wide-area network traffic. The XCache servers were backed with solid-state NVME drives deployed within the Kubernetes cluster nodes. All data access was authenticated using scitokens and was transparent to the user. To ensure we could track and measure data throughput precisely, we used our existing Prometheus monitoring stack to monitor the XCache pod throughput on the Kubernetes network layer. Using the rate query across all of the 8 XCache pods we were able to view a stacked cumulative graph of the total throughput for each XCache. This monitoring setup allowed us to ensure uniform data rates across all nodes while verifying we had reached the 200 Gbps benchmark.

hep-ex↗

arXiv2025

ML-based Adaptive Prefetching and Data Placement for US HEP Systems

Although benefits from caching in US HEP are well-known, current caching strategies are not adaptive i.e they do not adapt to changing cache access patterns. Newer developments such as the High-Luminosity - Large Hadron Collider (HL-LHC), Deep Underground Neutrino Experiment (DUNE), a steady move toward streaming readout based Data Acquisition systems (DAQs) will increase the data production exponentially and hence burden the storage, compute & network infrastructures. Moreover, existing caching frameworks are optimized to reduce latency, but not optimized for storage. This, in combination with limited cache capacities relative to total data, makes it difficult to achieve data locality. In this work, we present Machine Learning-aided (ML) caching strategies. Specifically, we first present a Long Short-Term Memory-based (LSTM) hourly and multi-step cache usage prediction. Second, we present an hourly file-level access prediction model based on CatboostRegressor. To date, most ML-based cache prediction strategies in HEP have focused on daily cache usage and limited works tackled hourly cache usage and even fewer strategies addressed hourly file-level access prediction. File-level access prediction allows for the design of intelligent prefetching and data placement strategies with fine-grained control. We validated our cache prediction strategies using data collected from SoCal MINI caches in August 2024. We are currently extending the WRENCH simulator to reflect the US HEP ecosystem at the storage, network and compute levels. We plan to deploy our cache prediction strategies into WRENCH and later perform extensive analysis with complex data access patterns and candidate infrastructure configurations.

cs.DC↗

arXiv2024

Adventures with Grace Hopper AI Super Chip and the National Research Platform

The National Science Foundation (NSF) funded National Research Platform (NRP) is a hyper-converged cluster of nationally and globally interconnected heterogeneous computing resources. The dominant computing environment of the NRP is the x86 64 instruction set architecture (ISA), often with graphics processing units (GPUs). Researchers across the nation leverage containers and Kubernetes to execute high-throughput computing (HTC) workloads across the heterogeneous cyberinfrastructure with minimal friction and maximum flexibility. As part of the NSF-funded GP-ENGINE project, we stood up the first server with an NVIDIA Grace Hopper AI Chip (GH200), an alternative ARM ISA, for the NRP. This presents challenges, as containers must be specifically built for ARM versus x86 64. Herein, we describe the challenges encountered, as well as our resulting solutions and some relevant performance benchmarks. We specifically compare the GH200 to A100 for computer vision workloads, within compute nodes in the NRP.

cs.DC↗

arXiv2023

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.

physics.comp-ph↗

arXiv2021

The Service Analysis and Network Diagnosis DataPipeline

Modern network performance monitoring toolkits, such as perfSONAR, take a remarkable number of measurements about the local network environment. To gain a complete picture of network performance, however, one needs to aggregate data across a large number of endpoints. The Service Analysis and Network Diagnosis (SAND) data pipeline collects data from diverse sources and ingests these measurements into a message bus. The message bus allows the project to send the data to multiple consumers, including a tape archive, an Elasticsearch database, and a peer infrastructure at CERN. In this paper, we explain the architecture and evolution of the SAND data pipeline, the scale of the resulting dataset, and how it supports a wide variety of network analysis applications.

cs.NI↗

arXiv2020

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.

cs.DC↗

arXiv2020

WLCG Networks: Update on Monitoring and Analytics

WLCG 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 and traffic routing. The OSG Networking Area, in partnership with WLCG, is focused on being the primary source of networking information for its partners and constituents. It was established to ensure sites and experiments can better understand and fix networking issues, while providing an analytics platform that aggregates network monitoring data with higher level workload and data trans-fer services. This has been facilitated by the global network of the perfSONAR instances that have been commissioned and are operated in collaboration with WLCG Network Throughput Working Group. An additional important updateis the inclusion of the newly funded NSF project SAND (Service Analytics and Network Diagnosis) which is focusing on network analytics. This paper describes the current state of the network measurement and analytics platform and summarizes the activities taken by the working group and our collaborators. This includes the progress being made in providing higher level analytics,alerting and alarming from the rich set of network metrics we are gathering.

cs.NI↗

arXiv2020

Trua: Efficient Task Replication for Flexible User-defined Availability in Scientific Grids

Failure is inevitable in scientific computing. As scientific applications and facilities increase their scales over the last decades, finding the root cause of a failure can be very complex or at times nearly impossible. Different scientific computing customers have varying availability demands as well as a diverse willingness to pay for availability. In contrast to existing solutions that try to provide higher and higher availability in scientific grids, we propose a model called Task Replication for User-defined Availability (Trua). Trua provides flexible, user-defined, availability in scientific grids, allowing customers to express their desire for availability to computational providers. Trua differs from existing task replication approaches in two folds. First, it relies on the historic failure information collected from the virtual layer of the scientific grids. The reliability model for the failures can be represented with a bimodal Johnson distribution which is different from any existing distributions. Second, it adopts an anomaly detector to filter out anomalous failures; it additionally adopts novel selection algorithms to mitigate the effects of temporary and spatial correlations of the failures without knowing the root cause of the failures. We apply the Trua on real-world traces collected from the Open Science Grid (OSG). Our results show that the Trua can successfully meet user-defined availability demands.

cs.DC↗

arXiv2020

Exploring Erasure Coding Techniques for High Availability of Intermediate Data

Scientific computing workflows generate enormous distributed data that is short-lived, yet critical for job completion time. This class of data is called intermediate data. A common way to achieve high data availability is to replicate data. However, an increasing scale of intermediate data generated in modern scientific applications demands new storage techniques to improve storage efficiency. Erasure Codes, as an alternative, can use less storage space while maintaining similar data availability. In this paper, we adopt erasure codes for storing intermediate data and compare its performance with replication. We also use the metric of Mean-Time-To-Data-Loss (MTTDL) to estimate the lifetime of intermediate data. We propose an algorithm to proactively relocate data redundancy from vulnerable machines to reliable ones to improve data availability with some extra network overhead. Furthermore, we propose an algorithm to assign redundancy units of data physically close to each other on the network to reduce the network bandwidth for reconstructing data when it is being accessed.

cs.DC↗

arXiv2019

Enabling Microsoft OneDrive Integration with HTCondor

Accessing data from distributed computing is essential in many workflows, but can be complicated for users of cyberinfrastructure. They must perform multiple steps to make data available to distributed computing using unfamiliar tools. Further, most research on data distribution has focused on the efficiency of providing data to computing resources rather than considering the ease of use for distributing data. Creating an easy to use data distribution method can reduce the time researchers spend learning cyberinfrastructure and increase its usefulness. Microsoft OneDrive is a online storage solution providing both file storage and sharing. OneDrive provides many different clients to access data stored in the service. It provides many features that users of cyberinfrastructure could find useful such as automatic synchronization with desktop clients. A barrier to using services such as OneDrive is the credential management necessary to access the service. Recent innovations in HTCondor have allowed the management of OAuth credentials to be handled by the scheduler on the user's behalf. The user no longer has to copy credentials along with the job, HTCondor will handle the acquisition, renewal, and secure transfer of credentials on the user's behalf. In this paper, I will focus on providing an easy to use data distribution method utilizing Microsoft OneDrive. Measuring ease of use is difficult, therefore I will will describe the features and advantages of using OneDrive. Additionally, I will compare it to measurements of data distribution methods currently used on a national cyberinfastructure, the Open Science Grid.

cs.DC↗

arXiv2019

SciTokens: Demonstrating Capability-Based Access to Remote Scientific Data using HTCondor

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. SciTokens introduces a capabilities-based authorization infrastructure for distributed scientific computing, to help scientists manage their security credentials more reliably and securely. SciTokens uses IETF-standard OAuth JSON Web Tokens for capability-based secure access to remote scientific data. These 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. In this extended abstract, we present the results over the past year of our open source implementation of the SciTokens model and its deployment in the Open Science Grid, including new OAuth support added in the HTCondor 8.8 release series.

cs.DC↗

arXiv2019

StashCache: A Distributed Caching Federation for the Open Science Grid

Data distribution for opportunistic users is challenging as they neither own the computing resources they are using or any nearby storage. Users are motivated to use opportunistic computing to expand their data processing capacity, but they require storage and fast networking to distribute data to that processing. Since it requires significant management overhead, it is rare for resource providers to allow opportunistic access to storage. Additionally, in order to use opportunistic storage at several distributed sites, users assume the responsibility to maintain their data. In this paper we present StashCache, a distributed caching federation that enables opportunistic users to utilize nearby opportunistic storage. StashCache is comprised of four components: data origins, redirectors, caches, and clients. StashCache has been deployed in the Open Science Grid for several years and has been used by many projects. Caches are deployed in geographically distributed locations across the U.S. and Europe. We will present the architecture of StashCache, as well as utilization information of the infrastructure. We will also present performance analysis comparing distributed HTTP Proxies vs StashCache.

cs.DC↗

arXiv2018

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.

cs.DC↗

arXiv2018

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.

cs.DC↗

arXiv2017

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.

cs.DC↗