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To address the need for high transfer throughput for projects such as the LHC experiments, including the upcoming HL-LHC, it is important to make optimal and sustainable use of our available capacity. Load balancing algorithms play a crucial role in distributing incoming network traffic across multiple servers, ensuring optimal resource utilization, preventing server overload, and enhancing performance and reliability. At the Rutherford Appleton Laboratory (RAL), the UK's Tier-1 centre for the Worldwide LHC Computing Grid (WLCG), we used XRootD's cluster management service component, which has an active load balancing algorithm to distribute traffic across 26 servers, but encountered its limitations when the system as a whole is under heavy load. We describe our attempted tuning of the configuration before describing proposing a new tunable tuneable, dynamic load-balancing algorithm balancer based on a weighted random selection algorithm.
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To address the needs of forthcoming projects such as the Square Kilometre Array (SKA) and the HL-LHC, there is a critical demand for data transfer nodes (DTNs) capable of achieving 100Gb/s transfer ratesof data movement. This high throughput can be attained through concurrency of transfers or by optimising the speed of individual transfers. At the Rutherford Appleton Laboratory (RAL), the UK's Tier-1 centre for the Worldwide LHC Computing Grid (WLCG), and a site for one of the UK SKA Regional Centres Centre (SRCsSRC), we have implemented 100GbE XRootD servers in anticipation of the SKA's operational scale-up. This presentation details the efforts undertaken to reach 100Gb/s data ingress and egress rates using the WebDAV protocol through XRootD endpoints. It The includes testing using a novel XRootD plug-in designed to assess asses XRootD performance independently of physical storage backendsbackend. The effects of tuning inside XRootD and by tuning the cephFS storage backend (e.g. via file layout) is presented. We will discuss the challenges encountered, bottlenecks identified, and insights gained, along with a description of the most effective solutions developed to date and highlights areas of future activities.
CHEP 2023:
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