Speaker
Description
Over the next few years, the LHC will prepare for the upcoming High-Luminosity upgrade
in which it is expected to deliver ten times more p-p collisions. This will create a harsher
radiation environment and higher detector occupancy. In this context, the ATLAS
experiment, one of the general purpose experiments at the LHC, plans substantial upgrades
to the detectors and to the trigger system in order to efficiently select events. Similarly, the
Data Acquisition System (DAQ) will have to redesign the data-flow architecture to
accommodate for the large increase in event and data rates.
The Phase-II DAQ design involves a large distributed storage system that buffers data read
out from the detector, while a computing farm (Event Filter) analyzes and selects the most
interesting events. This system will have to handle 5.2 TB/s of input data for an event rate of
1 MHz and provide access to 3 TB/s of these data to the filtering farm. A possible
implementation for such a design is based on distributed file systems (DFS) which are
becoming unavoidable among the big data industry. Features of DFS such as replication
strategies and smart placement policies match the distributed nature and the requirements of
the new data-flow system.
This paper presents an up-to-date performance evaluation of some of the DFS currently
available: GlusterFS, HadoopFS and CephFS. After characterization of the future data-flow
system’s workload, we report on small-scale raw performance and scalability studies.
Finally, we conclude on the suitability of such systems to the tight constraints expected for
the ATLAS experiment in phase-II and, in general, what the HEP community can profit
from these storage technologies.
