[Paper Review] Challenging the challenge: handling data in the Gigabit/s range

T. Antičić, J. P. Baud+19 more|ArXiv.org|May 16, 2003

Scientific Computing and Data Management2 references3 citations

TL;DR

This paper presents the ALICE Data Challenge, a critical test of data handling infrastructure for the ALICE experiment at CERN, demonstrating sustained data recording at over 300 MB/s—exceeding 1 Gb/s—using realistic processing pipelines and storage systems. It validates new technologies and frameworks for event building, data transfer, and permanent storage under extreme data rates.

ABSTRACT

The ALICE experiment at CERN will propose unprecedented requirements for event building and data recording. New technologies will be adopted as well as ad-hoc frameworks, from the acquisition of experimental data up to the transfer onto permanent media and its later access. These issues justify a careful, in-depth planning and preparation. The ALICE Data Challenge is a very important step of this development process where simulated detector data is moved from dummy data sources up to the recording media using processing elements and data-paths as realistic as possible. We will review herein the current status of past, present and future ALICE Data Challenges, with particular reference to the sessions held in 2002 when - for the first time - streams worth one week of ALICE data were recorded onto tape media at sustained rates exceeding 300 MB/s.

Motivation & Objective

• To validate the scalability and performance of data acquisition and storage systems under extreme data rates expected in the ALICE experiment.
• To test end-to-end data processing pipelines simulating real detector data flows from source to permanent storage.
• To identify bottlenecks and optimize system components such as event builders, data transfer mechanisms, and tape recording systems.
• To prepare for the operational demands of the LHC by simulating one week of ALICE data at gigabit-per-second rates.
• To demonstrate the feasibility of handling data streams exceeding 1 Gb/s using custom frameworks and emerging technologies.

Proposed method

• Simulating detector data streams from dummy sources to mimic real ALICE data production rates.
• Implementing a full data path including event building, data formatting, and transfer to permanent storage media.
• Using a distributed computing framework with processing elements designed for high-throughput data handling.
• Employing tape storage systems to record data at sustained rates exceeding 300 MB/s.
• Integrating custom software frameworks tailored for high-speed data acquisition and real-time processing.
• Conducting controlled test sessions with increasing data load to evaluate system stability and performance.

Experimental results

Research questions

• RQ1 Can a realistic data path sustain data rates exceeding 300 MB/s during end-to-end data recording?
• RQ2 What are the critical bottlenecks in event building and data transfer at gigabit-per-second data rates?
• RQ3 How effectively can custom software frameworks handle the volume and complexity of ALICE detector data?
• RQ4 Can tape storage systems reliably record one week of simulated ALICE data at sustained high rates?
• RQ5 What system configurations and technologies are required to achieve and maintain gigabit-per-second data throughput?

Key findings

• The ALICE Data Challenge successfully recorded one week of simulated ALICE data onto tape media at sustained rates exceeding 300 MB/s.
• The system achieved data rates approaching 1 Gb/s, validating the feasibility of handling extreme data volumes in high-energy physics experiments.
• The event-building and data transfer pipeline demonstrated robustness and scalability under high-load conditions.
• The use of custom frameworks and optimized data paths enabled efficient handling of data streams without significant loss or delay.
• The challenge confirmed that existing tape storage technologies could support the required data rates when properly integrated into the processing chain.
• The results provided critical insights for system tuning and informed the final design of ALICE's data acquisition and storage infrastructure.