Abstract

The Rutherford Appleton Laboratory (RAL) runs the UK Tier-1 which supports all the LHC experiments, as well as a growing number of others in HEP, Astronomy and Space Science. In September 2020, RAL was provided with funds to upgrade its network. The Tier-1 not only wants to meet the demands of LHC Run 3, it also wants to ensure that it can take an active role in data lake development and the network data challenges in the preparation for HL-LHC. It was therefore decided to completely rebuild the Tier-1 network with a Spine / Leaf architecture. This paper describes the network requirements and design decision that went into building the new Tier-1 network. It also includes a cost analysis, to understand if the ever increasing network requirements are deliverable in a continued flat cash environment and what limitations or opportunities this may place on future data lakes.

Highlights

  • The UK Tier-1 is located at the Rutherford Appleton Laboratory (RAL) in Oxfordshire and supports all LHC experiments, as well as a growing number of others in HEP, Astronomy and Space Science

  • This paper describes how the new RAL Tier-1 network was designed and implemented

  • The RAL Tier-1 has embarked on an ambitious project to completely rebuild its network to provide the throughput and features required for Run 3 and prototype data lakes

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Summary

Introduction

The UK Tier-1 is located at the Rutherford Appleton Laboratory (RAL) in Oxfordshire and supports all LHC experiments, as well as a growing number of others in HEP, Astronomy and Space Science. In September 2020, the RAL Tier-1 received £400, 000 from UK Research and Innovation’s (UKRI), World Class Laboratory fund which is intended for maintaining and refreshing existing UK scientific infrastructure, to ensure the scientific community’s ability to carry out exceptional science and to retain the country’s prominence in scientific research and output. This money has been invested replacing the RAL Tier-1 network. It discusses the cost implications as well as possible implications for data lakes

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