Frameworks to monitor and predict resource usage in the ATLAS High Level Trigger

The ATLAS High Level Trigger Farm consists of around 30,000 CPU cores which filter events at up to 100 kHz input rate. A costing framework is built into the high level trigger, this enables detailed monitoring of the system and allows for data-driven predictions to be made utilising specialist datasets. This talk will present an overview of how ATLAS collects in-situ monitoring data on both CPU usage and dataflow over the data-acquisition network during the trigger execution, and how these data are processed to yield both low level monitoring of individual selection-algorithms and high level data on the overall performance of the farm. For development and prediction purposes, ATLAS uses a special `Enhanced Bias' event selection. This mechanism will be explained along with how is used to profile expected resource usage and output event-rate of new physics selections, before they are executed on the actual high level trigger farm.