High Energy FermiLab Two physics detectors (5 stories tall each) to understand smallest scale of matter Each experiment has ~500 people doing science Each experiment handles millions of particle collisions per second - HUGE amount of data!

Data volume and analysis rates Total 4.3 petabytes Analysis >1 petabyte/month

The data challenge Use resources at participating institutions Ship and analyze data around the world ! Get data fast, processes it, and then immediately store the results back at Fermilab

Strategy Common business model for data cataloguing, tracking, and mining. Streamlined support of the underlying machinery Shared expertise solves issues at the user level. Follow grid standards, use grid middleware and shared resources (OSG and LCG grids). Contribute to grid projects OSG resource selection, SRM, security…

How we handle data Sequential access via Metadata ( SAM ) data storage, directly from the detector or from remote data processing facilities data cataloguing, mining distributed resources management to optimize usage and data throughput, and enforce the policies of the experiments. Use of variety storage service providers dCache, enstore, HPSS, SRM, in house disk resources

Success story: D0 refixing Problem. Correct processing mistake in 6 weeks 85 Tb and 4Million hours of 1Ghz CPU time Plenty of network but no free CPU to do the job at Fermilab Solution. Involve CPU resources elsewhere. Ship detector data directly to the analysis Cache re-usable data near computing sites OSG grid 20Tb LCG grid 5Tb Westgrid 10TbFermi 40TbIN2P3 10Tb

Conclusion Store a petabyte a year Process a petabyte a month Computing that meets growing demands of the HEP experiments one step ahead of the physics needs