The Virtual Research Environment is an analysis platform developed at CERN serving the needs of scientific communities involved in European Projects. Its scope is to facilitate the development of end-to-end physics workflows, providing researchers with access to an infrastructure and to the digital content necessary to produce and preserve a scientific result in compliance with FAIR principles. The platform's development is aimed at demonstrating how sciences spanning from High Energy Physics to Astrophysics could benefit from the usage of common technologies, initially born to satisfy CERN's exabyte-scale data management needs. The Virtual Research Environment's main components are (1) a federated distributed storage solution (the Data Lake), providing functionalities for data injection and replication through a Data Management framework (Rucio), (2) a computing cluster supplying the processing power to run full analyses with Reana, a re-analysis software, (3) a federated and reliable Authentication and Authorization layer and (4) an enhanced notebook interface with containerised environments to hide the infrastructure's complexity from the user. The deployment of the Virtual Research Environment is open-source and modular, in order to make it easily reproducible by partner institutions; it is publicly accessible and kept up to date by taking advantage of state of the art IT-infrastructure technologies.

The 26th International Conference on Computing in High Energy and Nuclear Physics (CHEP), organized by Jefferson Lab, took place in Norfolk, Virginia from 5–11 May 2023. The conference attracted 581 registered participants from 28 different countries. There were scientific presentations made over the 5 days of the conference. These were divided between 20 long talks and 2 keynotes, which were presented in plenary sessions; 450+ short talks, which were presented in parallel sessions; and 140+ posters split over two dedicated sessions.

One of the objectives of the ESCAPE collaboration, within the context of the implementation of the European Open Science Cloud, is integrating analysis workflows from Cosmology, Astrophysics and High Energy Physics into a common framework, making it easier for researchers to share data, software and analysis code. The development of the ESCAPE Science Projects (Dark Matter and Extreme Universe) relies on the implementation of the Virtual Research Environment, a prototype analysis platform deployed in the respect of FAIR data principles. The Virtual Research Environment’s main components are (1) an Authentication and Authorization layer, (2) a federated distributed storage solution, providing functionalities for data injection and replication through a Data Management and Orchestration software, (3) an interactive notebook interface that hides the complexity of the underlying infrastructure and facilitates browsing data catalogues and (4) computing processing power to run full end-to-end analyses through workflow schedulers and resource managers that also allow for pipelines’ preservation. This contribution will depict the collection of services and technologies that compose the Virtual Research Environment, hosted on a cloud infrastructure and managed by an open source, publicly available repository.

The data volumes stored in telescope archives is constantly increasing due to the development and improvements in the instrumentation. Often the archives need to be stored over a distributed storage architecture, provided by independent compute centres. Such a distributed data archive requires overarching data management orchestration. Such orchestration comprises of tools which handle data storage and cataloguing, and steering transfers integrating different storage systems and protocols, while being aware of data policies and locality. In addition, it needs a common Authorisation and Authentication Infrastructure (AAI) layer which is perceived as a single entity by end users and provides transparent data access. The scientific domain of particle physics also uses complex and distributed data management systems. The experiments at the Large Hadron Collider (LHC) accelerator at CERN generate several hundred petabytes of data per year. This data is globally distributed to partner sites and users using national compute facilities. Several innovative tools were developed to successfully address the distributed computing challenges in the context of the Worldwide LHC Computing Grid (WLCG). The work being carried out in the ESCAPE project and in the Data Infrastructure for Open Science (DIOS) work package is to prototype a Scientific Data Lake using the tools developed in the context of the WLCG, harnessing different physics scientific disciplines addressing FAIR standards and Open Data. We present how the Scientific Data Lake prototype is applied to address astronomical data use cases. We introduce the software stack and also discuss some of the differences between the domains.

The High Luminosity phase of the LHC, which aims for a tenfold increase in the luminosity of proton-proton collisions is expected to start operation in eight years. An unprecedented scientific data volume at the multiexabyte scale will be delivered to particle physics experiments at CERN. This amount of data has to be stored and the corresponding technology must ensure fast and reliable data delivery for processing by the scientific community all over the world. The present LHC computing model will not be able to provide the required infrastructure growth even taking into account the expected hardware evolution. To address this challenge the Data Lake R&D project has been launched by the DOMA community in the fall of 2019. State-of-the-art data handling technologies are under active development, and their current status for the Russian Scientific Data Lake prototype is presented here.

ESCAPE (European Science Cluster of Astronomy&Particle physics ESFRI research infrastructures) is an EU H2020 project that addresses the Open Science challenges shared by the astrophysics and and accelerator-based physics and nuclear physics ESFRI projects and landmarks. This project is embedded in the context of the European Open Science Cloud (EOSC) and involves activities to develop a prototype Data Lake and Science Platform, as well as support of an Open Source Software Repository, connection of the Virtual Observatory framework to EOSC, and engaging the public in citizen science. In this poster paper we provide a brief overview of the project and the results presented at ADASS.

HL-LHC will confront the WLCG community with enormous data storage, management and access challenges. These are as much technical as economical. In the WLCG-DOMA Access working group, members of the experiments and site managers have explored different models for data access and storage strategies to reduce cost and complexity, taking into account the boundary conditions given by our community.Several of these scenarios have been evaluated quantitatively, such as the Data Lake model and incremental improvements of the current computing model with respect to resource needs, costs and operational complexity.To better understand these models in depth, analysis of traces of current data accesses and simulations of the impact of new concepts have been carried out. In parallel, evaluations of the required technologies took place. These were done in testbed and production environments at small and large scale.We will give an overview of the activities and results of the working group, describe the models and summarise the results of the technology evaluation focusing on the impact of storage consolidation in the form of Data Lakes, where the use of streaming caches has emerged as a successful approach to reduce the impact of latency and bandwidth limitation.We will describe the experience and evaluation of these approaches in different environments and usage scenarios. In addition we will present the results of the analysis and modelling efforts based on data access traces of the experiments.

Storage has been identified as the main challenge for the future distributed computing infrastructures: Particle Physics (HL-LHC, DUNE, Belle-II), Astrophysics and Cosmology (SKA, LSST). In particular, the High Luminosity LHC (HL-LHC) will begin operations in the year of 2026 with expected data volumes to increase by at least an order of magnitude as compared with the present systems. Extrapolating from existing trends in disk and tape pricing, and assuming flat infrastructure budgets, the implications for data handling for end-user analysis are significant. HENP experiments need to manage data across a variety of mediums based on the types of data and its uses: from tapes (cold storage) to disks and solid state drives (hot storage) to caches (including world wide access data in clouds and "data lakes"). The DataLake R&D project aims at exploring an evolution of distributed storage while bearing in mind very high demands of the HL-LHC era. Its primary objective is to optimize hardware usage and operational costs of a storage system deployed across distributed centers connected by fat networks and operated as a single service. Such storage would host a large fraction of the data and optimize the cost, eliminating inefficiencies due to fragmentation. In this talk we will highlight current status of the project, its achievements, interconnection with other research activities in this field like WLCG-DOMA and ATLAS-Google DataOcean, and future plans.

The experiments at CERN’s Large Hadron Collider use the Worldwide LHC Computing Grid, the WLCG, for its distributed computing infrastructure. Through the distributed workload and data management systems, they provide seamless access to hundreds of grid, HPC and cloud based computing and storage resources that are distributed worldwide to thousands of physicists. LHC experiments annually process more than an exabyte of data using an average of 500,000 distributed CPU cores, to enable hundreds of new scientific results from the collider. However, the resources available to the experiments have been insufficient to meet data processing, simulation and analysis needs over the past five years as the volume of data from the LHC has grown. The problem will be even more severe for the next LHC phases. High Luminosity LHC will be a multiexabyte challenge where the envisaged Storage and Compute needs are a factor 10 to 100 above the expected technology evolution. The particle physics community needs to evolve current computing and data organization models in order to introduce changes in the way it uses and manages the infrastructure, focused on optimizations to bring performance and efficiency not forgetting simplification of operations. In this paper we highlight a recent R&D project related to scientific data lake and federated data storage.

WLCG DataLake R&D project aims at exploring an evolution of distributed storage while bearing in mind very high demands of HL-LHC era. Its primary objective is to optimize hardware usage and operational costs of a storage system deployed across distributed centres connected by fast networks and operated as a single service. Such storage could host a large fraction of the WLCG and other mega-science experiment's data while eliminating inefficiencies due to various levels of fragmentation. In this talk we will explain Russia's and in particular NRC "Kurchatov Institute's" role in the DataLake project with highlight on our goals, achievements and future plans.

The computing strategy document for HL-LHC identifies storage as one of the main WLCG challenges in one decade from now. In the naive assumption of applying today's computing model, the ATLAS and CMS experiments will need one order of magnitude more storage resources than what could be realistically provided by the funding agencies at the same cost of today. The evolution of the computing facilities and the way storage will be organized and consolidated will play a key role in how this possible shortage of resources will be addressed. In this contribution we will describe the architecture of a WLCG data lake, intended as a storage service geographically distributed across large data centers connected by fast network with low latency. Will present the experience with our first prototype, showing how the concept, implemented at different scales, can serve different needs, from regional and national consolidation of storage to an international data provisioning service. We will highlight how the system leverages its distributed nature, the economy of scale and different classes of storage to optimise the hardware and operational cost, through a set of policy driven decisions concerning data placement and data retention. We will discuss how the system leverages or interoperates with existing federated storage solutions. We will finally describe the possible data processing models in this environment and present our first benchmarks.

This paper summarizes ten years of operational experience of the WLCG Tier-1 computer centre at Port d’Informació Científica (PIC), which serves the ATLAS, CMS and LHCb experiments. The centre, located in Barcelona (Spain), has supported all of the commissioning activities be- fore the Large Hadron Collider (LHC) produced real collisions, it has continuously adapted to the new requirements, introducing new technologies as they became available, and it has grown significantly in resources offered to the LHC experiments, while maintaining top reliability levels as compared to other centres. Additional work has been done in the last years to reduce the power costs of the Tier-1 centre, and to prepare for the next challenges that are expected to come. Some thoughts on how the current WLCG system could evolve are also presented.

Dependability, resilience, adaptability and efficiency. Growing requirements require tailoring storage services and novel solutions. Unprecedented volumes of data coming from the broad number of experiments at CERN need to be quickly available in a highly scalable way for large-scale processing and data distribution while in parallel they are routed to tape for long-term archival. These activities are critical for the success of HEP experiments. Nowadays we operate at high incoming throughput (14GB/s during 2015 LHC Pb-Pb run and 11PB in July 2016) and with concurrent complex production work-loads. In parallel our systems provide the platform for the continuous user and experiment driven work-loads for large-scale data analysis, including end-user access and sharing. The storage services at CERN cover the needs of our community: EOS and CASTOR as a large-scale storage; CERNBox for end-user access and sharing; Ceph as data back-end for the CERN OpenStack infrastructure, NFS services and S3 functionality; AFS for legacy distributed-file-system services. In this paper we will summarise the experience in supporting LHC experiments and the transition of our infrastructure from static monolithic systems to flexible components providing a more coherent environment with pluggable protocols, tuneable QoS, sharing capabilities and fine grained ACLs management while continuing to guarantee dependable and robust services.

In the competitive market for large-scale storage solutions the current main disk storage system at CERN EOS has been showing its excellence in the multi-Petabyte high-concurrency regime. It has also shown a disruptive potential in powering the service in providing sync and share capabilities and in supporting innovative analysis environments along the storage of LHC data. EOS has also generated interest as generic storage solution ranging from university systems to very large installations for non-HEP applications.

EOS is an open source distributed filesystem developed and used mainly at CERN. It provides low latency, high availability, strong authentication, multiple replication schemas as well as multiple access protocols and features. Deployment and operations remain simple and EOS is currently being used by multiple experiments at CERN providing a total raw storage space of 86PB. A brief overview of EOS's architecture is given then its main and latest features are reviewed and some operations facts are reported. Finally, emphasis is laid on the new infrastructure aware file placement and access engine.

CERN IT DSS operates the main storage resources for data taking and physics analysis mainly via three system: AFS, CASTOR and EOS. The total usable space available on disk for users is about 100 PB (with relative ratios 1:20:120). EOS actively uses the two CERN Tier0 centres (Meyrin and Wigner) with 50:50 ratio. IT DSS also provide sizeable on-demand resources for IT services most notably OpenStack and NFS-based clients: this is provided by a Ceph infrastructure (3 PB) and few proprietary servers (NetApp). We will describe our operational experience and recent changes to these systems with special emphasis to the present usages for LHC data taking, the convergence to commodity hardware (nodes with 200-TB each with optional SSD) shared across all services. We also describe our experience in coupling commodity and home-grown solution (e.g. CERNBox integration in EOS, Ceph disk pools for AFS, CASTOR and NFS) and finally the future evolution of these systems for WLCG and beyond.

The CERN-IT Data Storage and Services (DSS) group stores and provides access to data coming from the LHC and other physics experiments. We implement specialised storage services to provide tools for optimal data management, based on the evolution of data volumes, the available technologies and the observed experiment and users' usage patterns. Our current solutions are CASTOR, for highly-reliable tape-backed storage for heavy-duty Tier-0 workflows, and EOS, for disk-only storage for full-scale analysis activities. CASTOR is evolving towards a simplified disk layer in front of the tape robotics, focusing on recording the primary data from the detectors. EOS is now a well-established storage service used intensively by the four big LHC experiments. Its conceptual design based on multi-replica and in-memory namespace, makes it the perfect system for data intensive workflows. The LHC-Long Shutdown 1 (LS1) presents a window of opportunity to shape up both of our storage services and validate against the ongoing analysis activity in order to successfully face the new LHC data taking period in 2015. In this paper, the current state and foreseen evolutions of CASTOR and EOS will be presented together with a study about the reliability of our systems.