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Machine learning (ML) and deep learning (DL) techniques are increasingly influential in High Energy Physics, necessitating effective computing infrastructures and training opportunities for users and developers, particularly concerning programmable hardware like FPGAs. A gap exists in accessible ML/DL on FPGA tutorials catering to diverse hardware specifications. To bridge this gap, collaborative efforts by INFN-Bologna, the University of Bologna, and INFN-CNAF produced a pilot course using virtual machines, inhouse cloud platforms, and AWS instances, utilizing Docker containers for interactive exercises. Additionally, the Bond Machine software ecosystem, capable of generating FPGA-synthesizable computer architectures, is explored as a simplified approach for teaching FPGA programming. [ABSTRACT FROM AUTHOR]

The INFN Tier1 data center is currently located in the premises of the Physics Department of the University of Bologna, where CNAF is also located. During 2023 it will be moved to the "Tecnopolo", the new facility for research, innovation, and technological development in the same city area; the same location is also hosting Leonardo, the pre-exascale supercomputing machine managed by CINECA, co-financed as part of the EuroHPC Joint Undertaking, 4th ranked in the top500 November 2022 list. The construction of the new CNAF data center consists of two phases, corresponding to the computing requirements of LHC: Phase 1 involves an IT power of 3 MW, and Phase 2, starting from 2025, involves an IT power up to 10 MW. The new data center is designed to cope with the computing requirements of the data taking of the HL-LHC experiments, in the time spanning from 2026 to 2040 and will provide, at the same time, computing services for several other INFN experiments and projects, not only belonging to the HEP domain. The co-location with Leonardo opens wider possibilities to integrate HTC and HPC resources and the new CNAF data center will be tightly coupled with it, allowing access from a single entry point to resources located at CNAF and provided by the supercomputer. Data access from both infrastructures will be transparent to users. In this presentation we describe the new data center design, providing a status update on the migration, and we focus on the Leonardo integration showing the results of the preliminary tests to access it from the CNAF access points. [ABSTRACT FROM AUTHOR]

INFN-CNAF is one of the Worldwide LHC Computing Grid (WLCG) Tier-1 data centres, providing computing, networking and storage resources to a wide variety of scientific collaborations, not limited to the four LHC (Large Hadron Collider) experiments. The INFN-CNAF data centre will move to a new location next year. At the same time, the requirements from our experiments and users are becoming increasingly challenging and new scientific communities have started or will soon start exploiting our resources. Currently, we are reengineering several services, in particular our monitoring infrastructure, in order to improve the day-by-day operations and to cope with the increasing complexity of the use cases and with the future expansion of the centre. This scenario led us to implement a data streaming infrastructure designed to enable log analysis, anomaly detection, threat hunting, integrity monitoring and incident response. Such data streaming platform has been organised to manage different kinds of data coming from heterogeneous sources, to support multi-tenancy and to be scalable. Moreover, we will be able to provide an on demand end-to-end data streaming application to those users/communities requesting such kind of facility. The infrastructure is based on the Apache Kafka platform, which provides streaming of events at large scale, with authorization and authentication configured at the topic level for ensuring data isolation and protection. Data can be consumed by different applications, such as those devoted to log analysis, which provide the capability to index large amounts of data and implement appropriate access policies to inspect and visualise information. In this contribution we will present and motivate our technological choices for the definition of the infrastructure, we will describe its components and we will depict use cases which can be addressed with this platform. [ABSTRACT FROM AUTHOR]

In modern data centres an effective and efficient monitoring system is a critical asset, yet a continuous concern for administrators. Since its birth, INFN Tier-1 data centre, hosted at CNAF, has used various monitoring tools all replaced, a few years ago, by a system common to all CNAF departments (based on Sensu, Influxdb, Grafana). Given the complexity of the inter-dependencies of the several services running at the data centre and the foreseen large increase of resources in the near future, a more powerful and versatile monitoring system is needed. This new monitoring system should be able to automatically correlate log files and metrics coming from heterogeneous sources and devices (including services, hardware and infrastructure) thus providing us with a suitable framework to implement a solution for the predictive analysis of the status of the whole environment. In particular, the possibility to correlate IT infrastructure monitoring information with the logs of running applications is of great relevance in order to be able to quickly find application failure root cause. At the same time, a modern, flexible and user-friendly analytics solution is needed in order to enable users, IT engineers and IT managers to extract valuable information from the different sources of collected data in a timely fashion. In this paper, a prototype of such a system, installed at the INFN Tier-1, is described with an assessment of the state and an evaluation of the resources needed for a fully production system. Technologies adopted, amount of foreseen data, target KPIs and production design are illustrated. [ABSTRACT FROM AUTHOR]

At the end of May 2017 the Globus Alliance announced that the open-source Globus Toolkit (GT) would be no longer supported by the Globus team at the University of Chicago. This announcement had an obvious impact on WLCG, given the central role of the Globus Security Infrastructure (GSI) and GridFTP in the WLCG data management framework, so discussions started in the appropriate forums on the search for alternatives. At the same time, support for token-based authentication and authorization has emerged as a key requirement for storage elements powering WLCG data centers. In this contribution, we describe the work done to enable token-based authentication and authorization in the StoRM WebDAV service, describing and highlighting the differences between support for external OpenID connect providers, groupbased and capability-based authorization schemes, and locally-issued authorization tokens. We discuss how StoRM WebDAV token-based authorization is being exploited in several contexts, from WLCG DOMA activities to other scientific experiments hosted at the INFN Tier-1 data center. In this contribution, we also describe the methodology used to compare Globus GridFTP and StoRM WebDAV and we present initial results confirming how HTTP represent a viable alternative to GridFTP for data transfers also performance-wise. [ABSTRACT FROM AUTHOR]

The INFN Tier-1 center at CNAF has been extended in 2016 and 2017 in order to include a small amount of resources (∼ 22 kHS06 corresponding to ∼ 10% of the CNAF pledges for LHC in 2017) physically located at the Bari-ReCas site (∼ 600 km distant from CNAF). In 2018, a significant fraction of the CPU power (∼ 170 kHS06, equivalent to ∼ 50% of the total CNAF pledges) is going to be provided via a collaboration with the PRACE Tier-0 CINECA center (a few km from CNAF), thus building a truly geographically distributed (WAN) center. The two sites are going to be interconnected via an high bandwidth link (400-1200 Gb/s), in order to ensure a transparent access to data residing on CNAF storage; the latency between the centers is small enough not to require particular caching strategies. In this contribution we describe the issues and the results of the production configuration, focusing both on the management aspects and on the performance provided to end-users. [ABSTRACT FROM AUTHOR]