With this contribution we present some recent developments made to Rucio, the data management system of the High-Energy Physics Experiment ATLAS. Already managing 300 Petabytes of both official and ...user data, Rucio has seen incremental improvements throughout LHC Run-2, and is currently laying the groundwork for HEP computing in the HL-LHC era. The focus of this contribution are (a) the automations that have been put in place such as data rebalancing or dynamic replication of user data, as well as their supporting infrastructures such as real-time networking metrics or transfer time predictions; (b) the flexible approach towards inclusion of heterogeneous storage systems, including object stores, while unifying the potential access paths using generally available tools and protocols; (c) machine learning approaches to help with transfer throughput estimation; and (d) the adoption of Rucio for two other experiments, AMS and Xenon1t. We conclude by presenting operational numbers and figures to quantify these improvements, and extrapolate the necessary changes and developments for future LHC runs.
Rucio is the next-generation Distributed Data Management (DDM) system benefiting from recent advances in cloud and "Big Data" computing to address HEP experiments scaling requirements. Rucio is an ...evolution of the ATLAS DDM system Don Quijote 2 (DQ2), which has demonstrated very large scale data management capabilities with more than 140 petabytes spread worldwide across 130 sites, and accesses from 1,000 active users. However, DQ2 is reaching its limits in terms of scalability, requiring a large number of support staff to operate and being hard to extend with new technologies. Rucio will deal with these issues by relying on a conceptual data model and new technology to ensure system scalability, address new user requirements and employ new automation framework to reduce operational overheads. We present the key concepts of Rucio, including its data organization/representation and a model of how to manage central group and user activities. The Rucio design, and the technology it employs, is described, specifically looking at its RESTful architecture and the various software components it uses. We show also the performance of the system.
The Production and Distributed Analysis system (PanDA), used for workload management in the ATLAS Experiment at the LHC for over a decade, has in recent years expanded its reach to diverse new ...resource types such as HPCs, and innovative new workflows such as the Event Service. PanDA meets the heterogeneous resources it harvests in the PanDA Pilot, which has embarked on a next-generation reengineering to efficiently integrate and exploit the new platforms and workflows. The new modular architecture is the product of a year of design and prototyping in conjunction with the design of a completely new component, Harvester, that will mediate a richer flow of control and information between Pilot and PanDA. Harvester will enable more intelligent and dynamic matching between processing tasks and resources, with an initial focus on HPCs, simplifying the operator and user view of a PanDA site but internally leveraging deep information gathering on the resource to accrue detailed knowledge of a site's capabilities and dynamic state to inform the matchmaking. This paper will give an overview of the new Pilot architecture, how it will be used in and beyond ATLAS, its relation to Harvester, and the work ahead.
The ATLAS Distributed Data Management (DDM) system has evolved drastically in the last two years with the Rucio software fully replacing the previous system before the start of LHC Run-2. The ATLAS ...DDM system manages now more than 250 petabytes spread on 130 storage sites and can handle file transfer rates of up to 30Hz. In this paper, we discuss our experience acquired in developing, commissioning, running and maintaining such a large system. First, we describe the general architecture of the system, our integration with external services like the WLCG File Transfer Service and the evolution of the system over its first years of production. Then, we show the performance of the system, describe the integration of new technologies such as object stores, and outline some new developments, which mainly focus on performance and automation.
This paper introduces a new dynamic data placement agent for the ATLAS distributed data management system. This agent is designed to pre-place potentially popular data to make it more widely ...available. It therefore incorporates information from a variety of sources. Those include input datasets and sites workload information from the ATLAS workload management system, network metrics from different sources like FTS and PerfSonar, historical popularity data collected through a tracer mechanism and more. With this data it decides if, when and where to place new replicas that then can be used by the WMS to distribute the workload more evenly over available computing resources and then ultimately reduce job waiting times. This paper gives an overview of the architecture and the final implementation of this new agent. The paper also includes an evaluation of the placement algorithm by comparing the transfer times and the new replica usage.
The ATLAS Distributed Data Management system stores more than 220PB of physics data across more than 130 sites globally. Rucio, the next generation data management system of the ATLAS collaboration, ...has now been successfully operated for two years. However, with the increasing workload and utilization, more automated and advanced methods of managing the data are needed. In this article we present an extension to the data management system, which is in charge of detecting and foreseeing storage elements reaching and surpassing their capacity limit. The system automatically and dynamically rebalances the data to other storage elements, while respecting and guaranteeing data distribution policies and ensuring the availability of the data. This concept not only lowers the operational burden, as these cumbersome procedures had previously to be done manually, but it also enables the system to use its distributed resources more efficiently, which not only affects the data management system itself, but in consequence also the workload management and production systems. This contribution describes the concept and architecture behind those components and shows the benefits made by the system.
Rucio is the next-generation of Distributed Data Management (DDM) system benefiting from recent advances in cloud and ”Big Data” computing to address HEP experiments scaling requirements. Rucio is an ...evolution of the ATLAS DDM system Don Quixote 2 (DQ2), which has demonstrated very large scale data management capabilities with more than 160 petabytes spread worldwide across 130 sites, and accesses from 1,000 active users. However, DQ2 is reaching its limits in terms of scalability, requiring a large number of support staff to operate and being hard to extend with new technologies. Rucio addresses these issues by relying on new technologies to ensure system scalability, cover new user requirements and employ new automation framework to reduce operational overheads. This paper shows the key concepts of Rucio, details the Rucio design, and the technology it employs, the tests that were conducted to validate it and finally describes the migration steps that were conducted to move from DQ2 to Rucio.
Input data for applications that run in cloud computing centres can be stored at distant repositories, often with multiple copies of the popular data stored at many sites. Locating and retrieving the ...remote data can be challenging, and we believe that federating the storage can address this problem. A federation would locate the closest copy of the data on the basis of GeoIP information. Currently we are using the dynamic data federation Dynafed, a software solution developed by CERN IT. Dynafed supports several industry standards for connection protocols like Amazon's S3, Microsoft's Azure, as well as WebDAV and HTTP. Dynafed functions as an abstraction layer under which protocol-dependent authentication details are hidden from the user, requiring the user to only provide an X509 certificate. We have setup an instance of Dynafed and integrated it into the ATLAS data distribution management system. We report on the challenges faced during the installation and integration. We have tested ATLAS analysis jobs submitted by the PanDA production system and we report on our first experiences with its operation.
This contribution details the deployment of Rucio, the ATLAS Distributed Data Management system. The main complication is that Rucio interacts with a wide variety of external services, and connects ...globally distributed data centres under different technological and administrative control, at an unprecedented data volume. It is therefore not possible to create a duplicate instance of Rucio for testing or integration. Every software upgrade or configuration change is thus potentially disruptive and requires fail-safe software and automatic error recovery. Rucio uses a three-layer scaling and mitigation strategy based on quasi-realtime monitoring. This strategy mainly employs independent stateless services, automatic failover, and service migration. The technologies used for deployment and mitigation include OpenStack, Puppet, Graphite, HAProxy and Apache. In this contribution, the interplay between these components, their deployment, software mitigation, and the monitoring strategy are discussed.
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