Online Production Validation in a HEP environment

TL;DR

This paper introduces innovative online monitoring techniques for high energy physics data processing in distributed grid environments, enabling real-time quality assessment to prevent errors and optimize resource use.

Contribution

It presents new methods for online data quality monitoring in a Grid environment, improving early error detection during large-scale HEP simulations.

Findings

Enhanced early error detection capabilities

Reduced time and resource wastage

Improved data quality assurance

Abstract

Petabytes of data are to be processed and stored requiring millions of CPU-years in high energy particle (HEP) physics event simulation. This enormous demand is handled in worldwide distributed computing centers as part of the LHC computing grid. These significant resources require a high quality and efficient production and the early detection of potential errors. In this article we present novel monitoring techniques in a Grid environment to collect quality measures during job execution. This allows online assessment of data quality information to avoid configuration errors or inappropriate settings of simulation parameters and therefore is able to save time and resources.