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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

FTS3: Data Movement Service in containers deployed in OKD [Slides]

The File Transfer Service (FTS3) is a data movement service developed at CERN which is used to distribute the majority of the Large Hadron Collider's data across the Worldwide LHC Computing Grid (WLCG) infrastructure. At Fermilab, we have deployed a couple of FTS3 instances for Intensity Frontier experiments (e.g. DUNE) to transfer data in America and Europe, using a container-based strategy. During this talk, we are going to present the two different configurations currently running at Fermilab, comparing and contrasting a Docker-based OKD deployment against a traditional RPM-based deployment and giving an overview of the possible issues encountered. In addition, we discuss our method of certificate management and maintenance utilizing Kubernetes cronjobs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

FTS3: Data Movement Service in containers deployed in OKD [Slides]

The File Transfer Service (FTS3) is a data movement service developed at CERN which is used to distribute the majority of the Large Hadron Collider's data across the Worldwide LHC Computing Grid (WLCG) infrastructure. At Fermilab, we have deployed FTS3 instances for Intensity Frontier experiments (e.g. DUNE) to transfer data in America and Europe, using a container-based strategy. In this article we summarize our experience building docker images based on work from the SLATE project (slateci.io) and deployed in OKD, the community distribution of Red Hat OpenShift. Additionally, we discuss our method of certificate management and maintenance utilizing Kubernetes CronJobs. Finally, we also report on the two different configurations currently running at Fermilab, comparing and contrasting a Docker-based OKD deployment against a traditional RPM-based deployment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

FTS3: Data Movement Service in containers deployed in OKD

The File Transfer Service (FTS3) is a data movement service developed at CERN which is used to distribute the majority of the Large Hadron Collider's data across the Worldwide LHC Computing Grid (WLCG) infrastructure. At Fermilab, we have deployed FTS3 instances for Intensity Frontier experiments (e.g. DUNE) to transfer data in America and Europe, using a container-based strategy. In this article we summarize our experience building docker images based on work from the SLATE project (slateci.io) and deployed in OKD, the community distribution of Red Hat OpenShift. Additionally, we discuss our method of certificate management and maintenance utilizing Kubernetes CronJobs. Finally, we also report on the two different configurations currently running at Fermilab, comparing and contrasting a Docker-based OKD deployment against a traditional RPM-based deployment.

Lobato Pardavila, Lorena↗
DOE OSTI2020

ServiceX A Distributed, Caching, Columnar Data Delivery Service

We will describe a component of the Intelligent Data Delivery Service being developed in collaboration with IRIS-HEP and the LHC experiments. ServiceX is an experiment-agnostic service to enable on-demand data delivery specifically tailored for nearly-interactive vectorized analysis. This work is motivated by the data engineering challenges posed by HL-LHC data volumes and the increasing popularity of python and Spark-based analysis workflows.ServiceX gives analyzers the ability to query events by dataset metadata. It uses containerized transformations to extract just the data required for the analysis. This operation is colocated with the data to avoid transferring unnecessary branches over the WAN. Simple filtering operations are supported to further reduce the amount of data transferred.Transformed events are cached in a columnar datastore to accelerate delivery of subsequent similar requests. ServiceX will learn commonly related columns and automatically include them in the transformation to increase the potential for cache hits by other users.Selected events are streamed to the analysis system using an efficient wire protocol that can be readily consumed by a variety of computational frameworks. This reduces time-to-insight for physics analysis by delegating to ServiceX the complexity of event selection, slimming, reformatting, and streaming.

Galewsky, B.↗
DOE OSTI2014

Data Bookkeeping Service 3 - Providing Event Metadata in CMS

The Data Bookkeeping Service 3 provides a catalog of event metadata for Monte Carlo and recorded data of the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) at CERN, Geneva. It comprises all necessary information for tracking datasets, their processing history and associations between runs, files and datasets, on a large scale of about 200, 000 datasets and more than 40 million files, which adds up in around 700 GB of metadata. The DBS is an essential part of the CMS Data Management and Workload Management (DMWM) systems [1], all kind of data-processing like Monte Carlo production, processing of recorded event data as well as physics analysis done by the users are heavily relying on the information stored in DBS.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

An intelligent Data Delivery Service for and beyond the ATLAS experiment

The intelligent Data Delivery Service (iDDS) has been developed to cope with the huge increase of computing and storage resource usage in the coming LHC data taking. It has been designed to intelligently orchestrate workflows and data management systems, decoupling data pre-processing, delivery, and primary processing in large scale workflows. It is an experiment-agnostic service that has been deployed to serve data carousel (orchestrating efficient processing of tape-resident data), machine learning hyperparameter optimization, active learning, and other complex multi-stage workflows defined via DAG (Directed Acyclic Graph), CWL (Common Workflow Language) and other descriptions, including a growing number of analysis workflows. We will at first introduce some deployed use cases in a summary. Then we will focus on new improvements and use cases under developments in ATLAS, Rubin Observatory and sPHENIX, together with future efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Semantic Catalog of Things, Services, and Data to Support a Wind Data Management Facility

The purpose of this paper is to discuss how community vocabularies and linked open data best practices are being used to seamlessly link things, data, and off the shelf services to support scientific offshore wind energy research for the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) Wind and Water Power Program. This is largely made possible by leveraging collaborative advances in the Internet of Things (IoT), Semantic Web, Linked Services, Linked Open Data (LOD), and RDF vocabulary communities, which provide the foundation for our design. By adapting these linked community best practices, we designed a wind characterization data management facility capable of continually collecting, processing, and preservation of in situ and remote sensing instrume

atmosphere↗
DOE OSTI2013

A Linked Fusion of Things, Services, and Data to Support a Collaborative Data Management Facility

The purpose of this paper is to illustrate the use of semantic technologies and approaches to seamlessly link things, services, and data in the proposed design of a scientific offshore wind energy research for the U.S. Department of Energy Wind and Water Technology Office of the Office of Energy Efficiency and Renewable Energy (EERE). By adapting linked community best practices, we were able to design a collaborative facility supporting both operational staff and end users that incorporates off-the-shelf components and overcome traditional barriers between devices, resulting data, and processing services. This was made largely possible through complementary advances in the Internet of Things (IoT), semantic web, Linked Services, and Linked Data communities, which provide the foundation for our design.

Stephan, Eric G.↗
DOE OSTI2015

Adding Data Management Services to Parallel File Systems

The objective of this project, called DAMASC for “Data Management in Scientific Computing”, is to coalesce data management with parallel file system management to present a declarative interface to scientists for managing, querying, and analyzing extremely large data sets efficiently and predictably. Managing extremely large data sets is a key challenge of exascale computing. The overhead, energy, and cost of moving massive volumes of data demand designs where computation is close to storage. In current architectures, compute/analysis clusters access data in a physically separate parallel file system and largely leave it scientist to reduce data movement. Over the past decades the high-end computing community has adopted middleware with multiple layers of abstractions and specialized file formats such as NetCDF-4 and HDF5. These abstractions provide a limited set of high-level data processing functions, but have inherent functionality and performance limitations: middleware that provides access to the highly structured contents of scientific data files stored in the (unstructured) file systems can only optimize to the extent that file system interfaces permit; the highly structured formats of these files often impedes native file system performance optimizations. We are developing Damasc, an enhanced high-performance file system with native rich data management services. Damasc will enable efficient queries and updates over files stored in their native byte-stream format while retaining the inherent performance of file system data storage via declarative queries and updates over views of underlying files. Damasc has four key benefits for the development of data-intensive scientific code: (1) applications can use important data-management services, such as declarative queries, views, and provenance tracking, that are currently available only within database systems; (2) the use of these services becomes easier, as they are provided within a familiar file-based ecosystem; (3) common optimizations, e.g., indexing and caching, are readily supported across several file formats, avoiding effort duplication; and (4) performance improves significantly, as data processing is integrated more tightly with data storage. Our key contributions are: SciHadoop which explores changes to MapReduce assumption by taking advantage of semantics of structured data while preserving MapReduce’s failure and resource management; DataMods which extends common abstractions of parallel file systems so they become programmable such that they can be extended to natively support a variety of data models and can be hooked into emerging distributed runtimes such as Stanford’s Legion; and Miso which combines Hadoop and relational data warehousing to minimize time to insight, taking into account the overhead of ingesting data into data warehousing.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

An intelligent Data Delivery Service for and beyond the ATLAS experiment

The intelligent Data Delivery Service (iDDS) has been developed to cope with the huge increase of computing and storage resource usage in the coming LHC data taking. iDDS has been designed to intelligently orchestrate workflow and data management systems, decoupling data pre-processing, delivery, and main processing in various workflows. It is an experiment-agnostic service around a workflow-oriented structure to work with existing and emerging use cases in ATLAS and other experiments. Here we will present the motivation for iDDS, its design schema and architecture, use cases and current status, and plans for the future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Integrated IO Services for Trilinos Data Structures

The Trilinos Project is an effort to develop algorithms and enabling technologies within an object- oriented software framework for the solution of large-scale, complex multi-physics engineering and scientific problems. A unique design feature of Trilinos is its focus on packages. While the abstractions make it easy to incorporate advanced processing and data manipulation tools, it is not always obvious how to take advantage of these features. The trios package incorporated two years ago offers general data management services, but has yet to offer integrated support for core Trilinos data structures, such as those offered in the Tpetra package. An initial attempt to incorporate native Trilinos data structure support into trios services revealed the complexity, from a non-mathematician's perspective, of using Trilinos. This project sought to understand the complexities and potential barriers not just for non-mathematicians that want to contribute to or use Trilinos, but potentially for new mathematically-inclined users as well that may want to offer services to support users. This report documents the challenges for trios to offer some simple data manipulation required as a precursor to any direct data services integration and makes recommendations for clarifying the performance implications and general approach to use.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A Survey on Next-generation Power Grid Data Architecture

The operation and control of power grids will increasingly rely on data. A high-speed, reliable, flexible and secure data architecture is the prerequisite of the next-generation power grid. This paper summarizes the challenges in collecting and utilizing power grid data, and then provides reference data architecture for future power grids. Based on the data architecture deployment, related research on data architecture is reviewed and summarized in several categories including data measurement/actuation, data transmission, data service layer, data utilization, as well as two cross-cutting issues, interoperability and cyber security. Research gaps and future work are also presented.

You, Shutang↗
DOE OSTI2012

The PhEDEx next-gen website

PhEDEx is the data-transfer management solution written by CMS. It consists of agents running at each site, a website for presentation of information, and a web-based data-service for scripted access to information. The website allows users to monitor the progress of data-transfers, the status of site agents and links between sites, and the overall status and behaviour of everything about PhEDEx. It also allows users to make and approve requests for data-transfers and for deletion of data. It is the main point-of-entry for all users wishing to interact with PhEDEx. For several years, the website has consisted of a single perl program with about 10K SLOC. This program has limited capabilities for exploring the data, with only coarse filtering capabilities and no context-sensitive awareness. Graphical information is presented as static images, generated on the server, with no interactivity. It is also not well connected to the rest of the PhEDEx codebase, since much of it was written before the data-service was developed. All this makes it hard to maintain and extend. We are re-implementing the website to address these issues. The UI is being rewritten in Javascript, replacing most of the server-side code. We are using the YUI toolkit to provide advanced features and context-sensitive interaction, and will adopt a Javascript charting library for generating graphical representations client-side. This relieves the server of much of its load, and automatically improves server-side security. The Javascript components can be re-used in many ways, allowing custom pages to be developed for specific uses. In particular, standalone test-cases using small numbers of components make it easier to debug the Javascript than it is to debug a large server program. Information about PhEDEx is accessed through the PhEDEx data-service, since direct SQL is not available from the clients browser. This provides consistent semantics with other, externally written monitoring tools, which already use the data-service. It also reduces redundancy in the code, yielding a simpler, consolidated codebase. In this talk we describe our experience of re-factoring this monolithic server-side program into a lighter client-side framework. We describe some of the techniques that worked well for us, and some of the mistakes we made along the way. We present the current state of the project, and its future direction.

Egeland, R.↗
DOE OSTI2019

Towards an Intelligent Data Delivery Service

The ATLAS Event Streaming Service (ESS) at the LHC is an approach to preprocess and deliver data for Event Service (ES) that has implemented a fine-grained approach for ATLAS event processing. The ESS allows one to asynchronously deliver only the input events required by ES processing, with the aim to decrease data traffic over WAN and improve overall data processing throughput. A prototype of ESS was developed to deliver streaming events to fine-grained ES jobs. Based on it, an intelligent Data Delivery Service (iDDS) is under development to decouple the "cold format" and the processing format of the data, which also opens the opportunity to include the production systems of other HEP experiments. Here we will at first present the ESS model view and its motivations for iDDS system. Then we will also present the iDDS schema, architecture and the applications of iDDS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Data Processing Unit Services Module

The Data Processing Services Module (DPUSM) provides the ability to perform pluggable compression, erasure coding, checksuming and other important file system operations within the Linux kernel. The pluggable provider interface allows for the use of hardware acceleration of those services. In-kernel file systems are then able to use these functions to use these accelerators to perform operations that are normally run on the processor, resulting in improved file system performance. Third parties will register "providers" with the DPUSM to communicate with their respective accelerators. Providers will implement functions with DPUSM API signatures so that the DPUSM can translate the data inputted by users of the DPUSM into data that providers recognize.

Lee, Jason↗
DOE OSTI2013

Meteorological services annual data report for 2012

This document presents the meteorological data collected at Brookhaven National Laboratory (BNL) by Meteorological Services (Met Services) for the calendar year 2012. The purpose is to publicize the data sets available to emergency personnel, researchers and facility operations. Met services has been collecting data at BNL since 1949. Data from 1994 to the present is available in digital format. Data is presented in monthly plots of one-minute data. This allows the reader the ability to peruse the data for trends or anomalies that may be of interest to them. Full data sets are available to BNL personnel and to a limited degree outside researchers. The full data sets allow plotting the data on expanded time scales to obtain greater details (e.g., daily solar variability, inversions, etc.).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Meteorological Services: Annual Data Report for 2013

This document presents the meteorological data collected at Brookhaven National Laboratory (BNL) by Meteorological Services (Met Services) for the calendar year 2013. The purpose is to publicize the data sets available to emergency personnel, researchers and facility operations. Met services has been collecting data at BNL since 1949. Data from 1994 to the present is available in digital format. Data is presented in monthly plots of one-minute data. This allows the reader the ability to peruse the data for trends or anomalies that may be of interest to them. Full data sets are available to BNL personnel and to a limited degree outside researchers based on the level of time and resources required. The full data sets allow plotting the data on expanded time scales to obtain greater details (e.g., daily solar variability, inversions, etc.).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Meteorological Services Annual Data Report for 2014

This document presents the meteorological data collected at Brookhaven National Laboratory (BNL) by Meteorological Services (Met Services) for the calendar year 2014. The purpose is to publicize the data sets available to emergency personnel, researchers and facility operations. Met services has been collecting data at BNL since 1949. Data from 1994 to the present is available in digital format. Data is presented in monthly plots of one-minute data. This allows the reader the ability to peruse the data for trends or anomalies that may be of interest to them. Full data sets are available to BNL personnel and to a limited degree outside researchers. The full data sets allow plotting the data on expanded time scales to obtain greater details (e.g., daily solar variability, inversions, etc.).

54 ENVIRONMENTAL SCIENCES↗