The ATLAS data flow

Simone Campana

CERN IT-SDC

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ATLAS Computing Model - Tiers

Implementation of the ATLAS computing model: tiers and clouds

ADC Retreat Napoli 1-4 February 2011

• Hierarchical tier organization based on Monarc network topology

• Sites are grouped into clouds for organizational reasons

• Possible communications:• Optical Private Network

• T0-T1• T1-T1

• National networks• Intra-cloud T1-T2

• Restricted communications: General public network

• Inter-cloud T1-T2• Inter-cloud T2-T2

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Detector Data Distribution

– RAW and reconstructed data generated at CERN and dispatched at T1s.

– Reconstructed data further replicated downstream to T2s of the SAME cloud

Tier-2Tier-2Tier-2Tier-2

Tier-0

Tier-1

Tier-2Tier-2Tier-2Tier-2

Tier-1

Tier-2

Tier-1

O(2to4GB) files(with exceptions)

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Data distribution after Reprocessing and Monte Carlo Reconstruction

– RAW data is re-processed at T1s to produce a new version of derived data

– Derived data are replicated to T1s of the same cloud

– Derived data are replicated to few other T1s (or CERN)

• And, from there, to other T2s of the same cloud

Tier-2Tier-2Tier-2

Tier-0

Tier-1

Tier-2Tier-2

Tier-1

O(2to4GB) files(with exceptions)

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Monte Carlo production

– Simulation (and some reconstruction) run at T2s

– Input data hosted at T1s is transferred (and cached) at T2s

– Output data are copied and stored back to T1s

– For reconstruction, derived data are

• replicated to few other T1s (or CERN)

• And, from there, to other T2s of the same cloud

Tier-2Tier-2Tier-2

Tier-0

Tier-1Tier-1

INPUT

OUTPUT

Simone Campana – ATLAS TIM Tokyo 7

Analysis• The paradigm is “jobs go to data” i.e.

– Jobs are brokered at sites where data have been pre-placed

– Jobs access data only from the local storage of the site where they run

– Jobs store the output in the storage the site where they run

• No WAN involved.

Simone Campana – ATLAS TIM Tokyo 8

Issues - I• You need data at some T2

(normally “your” T2)• The inputs are at some

other T2 in a different cloud

• Examples:– Outputs of analysis jobs– Replication of particular

samples on demandTier-2

Tier-2

Tier-1Tier-1

According to the model you should:

Simone Campana – ATLAS TIM Tokyo 9

Issues - II• You need to process

data available only at a give T1

• All sites of that cloud are very busy

• You assign jobs to some T2 of a different cloud

Tier-2

Tier-1Tier-1

INPUT

OUTPUTAccording to the model you should:

Simone Campana – ATLAS TIM Tokyo 10

Evolution of the model• ATLAS decided to relax the monarch model

– Allow T1-T2 and T2-T2 traffic between different clouds

• Any site can exchange data with any site if the system believes it is convenient– The “convenience” is measured in terms of per-file

transfer rate, between direct transfers and multi-hopped transfer

Simone Campana – ATLAS TIM Tokyo 11

Evolution of the model

Tier-2Tier-2

Tier-1Tier-1

Tier-2

Tier-1Tier-1

Multi-Cloud Monte Carlo production Analysis Output

Simone Campana – ATLAS TIM Tokyo 12

How to choose a transfer path?

• Currently– If a source file is available in the same cloud as destination,

transfer directly– If a source file is available at another cloud

• Compute the time it would take transferring directly• Compute the time it would take creating intermediate replica(s) at T1(s)• Times are computed based on previous transfers between the same sites• Take the most “time convenient” option

• In future– We intend to eliminate the separation between cloud and non

cloud sites– We intend to eliminate the creation of intermediate replicas (and

use always direct transfers)

Simone Campana – ATLAS TIM Tokyo 13

To ConcludeEvolution of the model and impact on the connectivity

for a large T2 (like Tokyo)

• So far ATLAS asked (large) T2s– To be well connected to their T1– To be well connected to the T2s of their cloud

• Now we are asking large T2s:– To be well connected to all T1s– To foresee non negligible traffic from/to other (large) T2s